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: "fundamental limits and scaling behavior of cooperative multicasting in wireless networks"
  Abstract: A framework is developed for analyzing capacity gains from user cooperation in slow fading wireless networks when the number of nodes (network size) is large. The framework is illustrated for the case of a simple multipath-rich Rayleigh fading channel model. Both unicasting (one source and one destination) and multicasting (one source and several destinations) scenarios are considered. We introduce a meaningful notion of Shannon capacity for such systems, evaluate this capacity as a function of signal-to-noise ratio (SNR), and develop a simple two-phase cooperative network protocol that achieves it. We observe that the resulting capacity is the same for both unicasting and multicasting, but show that the network size required to achieve any target error probability is smaller for unicasting than for multicasting. Finally, we introduce the notion of a network ``scaling exponent'' to quantify the rate of decay of error probability with network size as a function of the targeted fraction of the capacity. This exponent provides additional insights to system designers by enabling a finer grain comparison of candidate cooperative transmission protocols in even moderately sized networks.

Connected through hyperedge HE-1 (induced by source paper "device to device aided multicasting"):
  with members:
    - "scalable content delivery with coded caching in multi antenna fading channels"
    - "wireless caching technical misconceptions and business barriers"
    - "fundamental limits of caching"

Connected through hyperedge HE-2 (induced by source paper "an information theoretic analysis of the gaussian multicast channel with interactive user cooperation"):
  with members:
    - "enhancing multiuser mimo through opportunistic d2d cooperation"
    - "on the achievable rates of multihop virtual full duplex relay channels"
    - "noisy network coding with partial df"
    - "a survey on device to device communication in cellular networks"
    - "massive mimo multicasting in noncooperative cellular networks"
    - "spectrum sharing for device to device communication in cellular networks"
    - "modeling analysis and optimization of multicast device to device transmissions"
    - "short message noisy network coding with rate splitting"

Connected through hyperedge HE-3 (induced by source paper "mixed noisy network coding and cooperative unicasting in wireless networks"):
  with members:
    - "short message noisy network coding with a decode forward option"
    - "a unified relay framework with both d f and c f relay nodes"
    - "compute and forward strategies for cooperative distributed antenna systems"
    - "selective coding strategy for unicast composite networks"
    - "wireless network information flow a deterministic approach"
    - "the degrees of freedom of compute and forward"
    - "short message quantize forward network coding"
    - "slepian wolf coding over cooperative relay networks"

Connected through hyperedge HE-4 (induced by source paper "on the broadcast latency in finite cooperative wireless networks"):
  with members:
    - "distance distributions in finite uniformly random networks theory and applications"
    - "a geometric interpretation of fading in wireless networks theory and applications"

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)