human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "modeling and analysis of k tier downlink heterogeneous cellular networks"
  Abstract: Cellular networks are in a major transition from a carefully planned set of large tower-mounted base-stations (BSs) to an irregular deployment of heterogeneous infrastructure elements that often additionally includes micro, pico, and femtocells, as well as distributed antennas. In this paper, we develop a tractable, flexible, and accurate model for a downlink heterogeneous cellular network (HCN) consisting of K tiers of randomly located BSs, where each tier may differ in terms of average transmit power, supported data rate and BS density. Assuming a mobile user connects to the strongest candidate BS, the resulting Signal-to-Interference-plus-Noise-Ratio (SINR) is greater than 1 when in coverage, Rayleigh fading, we derive an expression for the probability of coverage (equivalently outage) over the entire network under both open and closed access, which assumes a strikingly simple closed-form in the high SINR regime and is accurate down to -4 dB even under weaker assumptions. For external validation, we compare against an actual LTE network (for tier 1) with the other K-1 tiers being modeled as independent Poisson Point Processes. In this case as well, our model is accurate to within 1-2 dB. We also derive the average rate achieved by a randomly located mobile and the average load on each tier of BSs. One interesting observation for interference-limited open access networks is that at a given \sinr, adding more tiers and/or BSs neither increases nor decreases the probability of coverage or outage when all the tiers have the same target-SINR.

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning based cooperative coded caching under dynamic popularities in ultra dense networks"):
  with members:
    - "spectrum sharing in vehicular networks based on multi agent reinforcement learning"
    - "deep learning based channel estimation for massive mimo with mixed resolution adcs"
    - "deep learning based wireless resource allocation with application to vehicular networks"
    - "using grouped linear prediction and accelerated reinforcement learning for online content caching"
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "multi agent learning for cooperative large scale caching networks"
    - "deep reinforcement learning based resource allocation for v2v communications"
    - "random caching based cooperative transmission in heterogeneous wireless networks"

Connected through hyperedge HE-2 (induced by source paper "enhancing physical layer security of random caching in large scale multi antenna heterogeneous wireless networks"):
  with members:
    - "secure and energy efficient transmissions in cache enabled heterogeneous cellular networks performance analysis and optimization"
    - "enhancing performance of random caching in large scale wireless networks with multiple receive antennas"
    - "physical layer security in cache enabled cooperative small cell networks against randomly distributed eavesdroppers"
    - "a unified framework for the tractable analysis of multi antenna wireless networks"
    - "enhancing performance of random caching in large scale heterogeneous wireless networks with random discontinuous transmission"
    - "delivery secrecy tradeoff for cache enabled stochastic networks content placement optimization"
    - "random caching based cooperative transmission in heterogeneous wireless networks"
    - "caching at the wireless edge design aspects challenges and future directions"

Connected through hyperedge HE-3 (induced by source paper "dynamic tdd interference analysis in macro cell and small cell deployments"):
  with members:
    - "3d beamforming based dynamic tdd interference mitigation scheme"
    - "interference analysis in dynamic tdd system combined or not with cell clustering scheme"
    - "performance of dynamic and static tdd in self backhauled millimeter wave cellular networks"
    - "a primer on cellular network analysis using stochastic geometry"
    - "analytical tractability of hexagonal network model with random user location"
    - "d2d enhanced heterogeneous cellular networks with dynamic tdd"
    - "dynamic uplink downlink optimization in tdd based small cell networks"
    - "analytical modeling of uplink cellular networks"

Connected through hyperedge HE-4 (induced by source paper "handover probability in drone cellular networks"):
  with members:
    - "fundamentals of drone cellular network analysis under random waypoint mobility model"
    - "performance characterization of canonical mobility models in drone cellular networks"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "integrating uavs into existing wireless networks a stochastic geometry approach"
    - "cyclical multiple access in uav aided communications a throughput delay tradeoff"
    - "spatio temporal interference correlation and joint coverage in cellular networks"

Connected through hyperedge HE-5 (induced by source paper "smart radio environments empowered by ai reconfigurable meta surfaces an idea whose time has come"):
  with members:
    - "a novel communication paradigm for high capacity and security via programmable indoor wireless environments in next generation wireless systems"
    - "reflection probability in wireless networks with metasurface coated environmental objects an approach based on random spatial processes"
    - "a tractable analysis of the blind spot probability in localization networks under correlated blocking"
    - "using any surface to realize a new paradigm for wireless communications"
    - "wireless network intelligence at the edge"
    - "medium access control in wireless network on chip a context analysis"
    - "beyond massive mimo the potential of positioning with large intelligent surfaces"
    - "realizing wireless communication through software defined hypersurface environments"

Connected through hyperedge HE-6 (induced by source paper "millimeter wave integrated access and backhaul in 5g performance analysis and design insights"):
  with members:
    - "integrated access and backhaul in 5g mmwave networks potentials and challenges"
    - "a tractable analysis of the blind spot probability in localization networks under correlated blocking"
    - "end to end simulation of integrated access and backhaul at mmwaves"
    - "max min rates in self backhauled millimeter wave cellular networks"
    - "distributed path selection strategies for integrated access and backhaul at mmwaves"
    - "modeling and analyzing millimeter wave cellular systems"
    - "integrated mmwave access and backhaul in 5g bandwidth partitioning and downlink analysis"
    - "capacity scaling of cellular networks impact of bandwidth infrastructure density and number of antennas"

Connected through hyperedge HE-7 (induced by source paper "nuts and bolts of a realistic stochastic geometric analysis of mmwave hetnets hardware impairments and channel aging"):
  with members:
    - "toward a realistic assessment of multiple antenna hcns residual additive transceiver hardware impairments and channel aging"
    - "stochastic geometric coverage analysis in mmwave cellular networks with a realistic channel model"
    - "impact of general channel aging conditions on the downlink performance of massive mimo"
    - "rate splitting to mitigate residual transceiver hardware impairments in massive mimo systems"
    - "downlink mimo hcns with residual transceiver hardware impairments"
    - "hybrid architectures with few bit adc receivers achievable rates and energy rate tradeoffs"
    - "achievable rate and energy efficiency of hybrid and digital beamforming receivers with low resolution adc"
    - "coverage in heterogeneous downlink millimeter wave cellular networks"

Connected through hyperedge HE-8 (induced by source paper "edge computing enabled cell free massive mimo systems"):
  with members:
    - "mobile edge computing enabled heterogeneous networks"
    - "fog massive mimo a user centric seamless hot spot architecture"
    - "joint energy minimization and resource allocation in c ran with mobile cloud"
    - "a survey on mobile edge computing the communication perspective"
    - "channel hardening and favorable propagation in cell free massive mimo with stochastic geometry"
    - "energy efficient resource allocation for mobile edge computation offloading"
    - "collaborative mobile edge computing in 5g networks new paradigms scenarios and challenges"
    - "power delay tradeoff in multi user mobile edge computing systems"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.IT(Information Theory)