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: "joint uplink and downlink coverage analysis of cellular based rf powered iot network"
  Abstract: Ambient radio frequency (RF) energy harvesting has emerged as a promising solution for powering small devices and sensors in massive Internet of Things (IoT) ecosystem due to its ubiquity and cost efficiency. In this paper, we study joint uplink and downlink coverage of cellular-based ambient RF energy harvesting IoT where the cellular network is assumed to be the only source of RF energy. We consider a time division-based approach for power and information transmission where each time-slot is partitioned into three sub-slots: (i) charging sub-slot during which the cellular base stations (BSs) act as RF chargers for the IoT devices, which then use the energy harvested in this sub-slot for information transmission and/or reception during the remaining two sub-slots, (ii) downlink sub-slot during which the IoT device receives information from the associated BS, and (iii) uplink sub-slot during which the IoT device transmits information to the associated BS. For this setup, we characterize the joint coverage probability, which is the joint probability of the events that the typical device harvests sufficient energy in the given time slot and is under both uplink and downlink signal-to-interference-plus-noise ratio (SINR) coverage with respect to its associated BS. This metric significantly generalizes the prior art on energy harvesting communications, which usually focused on downlink or uplink coverage separately. The key technical challenge is in handling the correlation between the amount of energy harvested in the charging sub-slot and the information signal quality (SINR) in the downlink and uplink sub-slots. Dominant BS-based approach is developed to derive tight approximation for this joint coverage probability. Several system design insights including comparison with regularly powered IoT network and throughput-optimal slot partitioning are also provided.

Connected through hyperedge HE-1 (induced by source paper "joint energy and sinr coverage in spatially clustered rf powered iot network"):
  with members:
    - "optimization of energy constrained wireless powered communication networks with heterogeneous nodes"
    - "3gpp inspired hetnet model using poisson cluster process sum product functionals and downlink coverage"
    - "poisson cluster process bridging the gap between ppp and 3gpp hetnet models"
    - "non orthogonal multiple access schemes in wireless powered communication networks"
    - "enriched k tier hetnet model to enable the analysis of user centric small cell deployments"
    - "poisson cluster process based analysis of hetnets with correlated user and base station locations"
    - "on optimal policies in full duplex wireless powered communication networks"
    - "wirelessly powered backscatter communication networks modeling coverage and capacity"

Connected through hyperedge HE-2 (induced by source paper "backscatter communications for the internet of things a stochastic geometry approach"):
  with members:
    - "backscatter communications for internet of things theory and applications"
    - "multistatic scatter radio sensor networks for extended coverage"
    - "backscatter communications for wireless powered sensor networks with collision resolution"
    - "modeling and analysis of cellular networks using stochastic geometry a tutorial"
    - "downlink and uplink decoupling in two tier heterogeneous networks with multi antenna base stations"
    - "modulation in the air backscatter communication over ambient ofdm carrier"
    - "waveform design for wireless power transfer"
    - "the internet of things new interoperability management and security challenges"

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)