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: "capacity of a class of broadcast relay channels"
  Abstract: Consider the broadcast relay channel (BRC) which consists of a source sending information over a two user broadcast #R##N#channel in presence of two relay nodes that help the transmission to the destinations. Clearly, this network with #R##N#five nodes involves all the problems encountered in relay and broadcast channels. New inner bounds on the capacity #R##N#region of this class of channels are derived. These results can be seen as a generalization and hence unification of #R##N#previous work in this topic. Our bounds are based on the idea of recombination of message bits and various effective #R##N#coding strategies for relay and broadcast channels. Capacity result is obtained for the semi-degraded BRC-CR, where #R##N#one relay channel is degraded while the other one is reversely degraded. An inner and upper bound is also presented #R##N#for the degraded BRC with common relay (BRC-CR), where both the relay and broadcast channel are degraded which is #R##N#the capacity for the Gaussian case. Application of these results arise in the context of opportunistic cooperation #R##N#of cellular networks.

Connected through hyperedge HE-1 (induced by source paper "cooperative strategies for simultaneous and broadcast relay channels"):
  with members:
    - "a note on outer bounds for broadcast channel"
    - "broadcasting over the relay channel with oblivious cooperative strategy"
    - "compute and forward harnessing interference through structured codes"
    - "the impact of csi and power allocation on relay channel capacity and cooperation strategies"
    - "capacity of a class of modulo sum relay channels"
    - "rate regions for relay broadcast channels"
    - "broadcast channels with cooperating decoders"
    - "cooperative relay broadcast channels"

Connected through hyperedge HE-2 (induced by source paper "relay strategies based on cross determinism for the broadcast relay channel"):
  with members:
    - "wireless network information flow a deterministic approach"
    - "coarse network coding a simple relay strategy to resolve interference"
    - "capacity of a class of deterministic relay channels"
    - "capacity of a class of modulo sum relay channels"
    - "rate regions for relay broadcast channels"

Connected through hyperedge HE-3 (induced by source paper "broadcasting over the relay channel with oblivious cooperative strategy"):
  with members:
    - "rate regions for relay broadcast channels"
    - "cooperative relay broadcast channels"

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)