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: "tractable resource management with uplink decoupled millimeter wave overlay in ultra dense cellular networks"
  Abstract: The forthcoming 5G cellular network is expected to overlay millimeter-wave (mmW) transmissions with the incumbent micro-wave (  $\mu\text{W}$  ) architecture. The overall mm-  $\mu\text{W}$   resource management should, therefore, harmonize with each other. This paper aims at maximizing the overall downlink (DL) rate with a minimum uplink (UL) rate constraint, and concludes: mmW tends to focus more on DL transmissions while   $\mu\text{W}$   has high priority for complementing UL, under time-division duplex (TDD) mmW operations. Such UL dedication of   $\mu\text{W}$   results from the limited use of mmW UL bandwidth due to excessive power consumption and/or high peak-to-average power ratio (PAPR) at mobile users. To further relieve this UL bottleneck, we propose mmW UL decoupling that allows each legacy   $\mu\text{W}$   base station (BS) to receive mmW signals. Its impact on mm-  $\mu\text{W}$   resource management is provided in a tractable way by virtue of a novel closed-form mm-  $\mu\text{W}$   spectral efficiency (SE) derivation. In an ultra-dense cellular network (UDN), our derivation verifies mmW (or   $\mu\text{W}$  ) SE is a logarithmic function of BS-to-user density ratio. This strikingly simple yet practically valid analysis is enabled by exploiting stochastic geometry in conjunction with real three-dimensional (3-D) building blockage statistics in Seoul, South Korea.

Connected through hyperedge HE-1 (induced by source paper "wireless edge computing with latency and reliability guarantees"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "how reliable and capable is multi connectivity"
    - "a tutorial on beam management for 3gpp nr at mmwave frequencies"
    - "uav communications for 5g and beyond recent advances and future trends"
    - "coded federated computing in wireless networks with straggling devices and imperfect csi"
    - "ultra reliable communication in 5g mmwave networks a risk sensitive approach"
    - "on device federated learning via blockchain and its latency analysis"

Connected through hyperedge HE-2 (induced by source paper "sense and predict harnessing spatial interference correlation for cognitive radio networks"):
  with members:
    - "ultra reliable and low latency communications in 5g downlink physical layer aspects"
    - "millimeter wave interference avoidance via building aware associations"
    - "the connectivity of millimeter wave networks in urban environments modeled using random lattices"
    - "macro diversity in cellular networks with random blockages"
    - "sense and predict opportunistic mac based on spatial interference correlation for cognitive radio networks"
    - "downlink and uplink cell association with traditional macrocells and millimeter wave small cells"
    - "spatio temporal interference correlation and joint coverage in cellular networks"
    - "design and analysis of initial access in millimeter wave cellular networks"

Connected through hyperedge HE-3 (induced by source paper "distilling on device intelligence at the network edge"):
  with members:
    - "gadmm fast and communication efficient framework for distributed machine learning"
    - "federated reinforcement distillation with proxy experience memory"
    - "multi hop federated private data augmentation with sample compression"
    - "robust coreset construction for distributed machine learning"
    - "federated machine learning concept and applications"
    - "wireless network intelligence at the edge"
    - "large scale distributed neural network training through online distillation"
    - "adaptive federated learning in resource constrained edge computing systems"

Connected through hyperedge HE-4 (induced by source paper "one pixel image and rf signal based split learning for mmwave received power prediction"):
  with members:
    - "proactive received power prediction using machine learning and depth images for mmwave networks"
    - "minimizing latency to support vr social interactions over wireless cellular systems via bandwidth allocation"
    - "ultra reliable and low latency wireless communication tail risk and scale"

Connected through hyperedge HE-5 (induced by source paper "stochastic geometric coverage analysis in mmwave cellular networks with realistic channel and antenna radiation models"):
  with members:
    - "study of realistic antenna patterns in 5g mmwave cellular scenarios"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "coverage and connectivity analysis of millimeter wave vehicular networks"
    - "millimeter wave interference avoidance via building aware associations"
    - "modeling and analyzing millimeter wave cellular systems"
    - "coverage analysis for millimeter wave networks the impact of directional antenna arrays"
    - "stochastic geometric coverage analysis in mmwave cellular networks with a realistic channel model"
    - "correction factor for analysis of mimo wireless networks with highly directional beamforming"

Connected through hyperedge HE-6 (induced by source paper "content placement in cache enabled sub 6 ghz and millimeter wave multi antenna dense small cell networks"):
  with members:
    - "random caching based cooperative transmission in heterogeneous wireless networks"
    - "modeling and analyzing millimeter wave cellular systems"
    - "caching meets millimeter wave communications for enhanced mobility management in 5g networks"
    - "caching policy toward maximal success probability and area spectral efficiency of cache enabled hetnets"
    - "a tractable framework for performance analysis of dense multi antenna networks"
    - "a new look at physical layer security caching and wireless energy harvesting for heterogeneous ultra dense networks"
    - "antenna count for massive mimo 1 9 ghz versus 60 ghz"
    - "on optimal geographical caching in heterogeneous cellular networks"

Connected through hyperedge HE-7 (induced by source paper "fast and reliable initial access with random beamforming for mmwave networks"):
  with members:
    - "a tutorial on beam management for 3gpp nr at mmwave frequencies"
    - "reducing initial cell search latency in mmwave networks"
    - "directional cell search delay analysis for cellular networks with static users"
    - "design and analysis of initial access in millimeter wave cellular networks"
    - "initial access in 5g mmwave cellular networks"
    - "performance analysis of outdoor mmwave ad hoc networks"
    - "context information based initial cell search for millimeter wave 5g cellular networks"
    - "context information for fast cell discovery in mm wave 5g networks"

Connected through hyperedge HE-8 (induced by source paper "mean field game theoretic edge caching in ultra dense networks"):
  with members:
    - "ultra dense edge caching under spatio temporal demand and network dynamics"
    - "spatially correlated content caching for device to device communications"
    - "ultra dense small cell networks turning density into energy efficiency"
    - "fundamental storage latency tradeoff in cache aided mimo interference networks"
    - "caching in wireless small cell networks a storage bandwidth tradeoff"
    - "wireless content caching for small cell and d2d networks"
    - "mean field games for distributed caching in ultra dense small cell networks"
    - "beyond the ultra dense barrier paradigm shifts on the road beyond 1000x wireless capacity"

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)