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: "interference channel capacity region for randomized fixed composition codes"
  Abstract: The randomized fixed-composition codes with optimal decoding error exponents are recently studied in [11], [12] for the finite alphabet interference channel with two transmitter-receiver pairs. In this paper we investigate the capacity region for randomized fixed-composition codes. A complete characterization of the capacity region of the said coding scheme is given. The inner bound is derived by showing the existence of a positive error exponent within the capacity region. A simple universal decoding rule is given. The tight outer bound is derived by extending a technique first developed in [10] for single input output channels to interference channels. It is shown that even with a sophisticated time-sharing scheme among randomized fixed-composition codes, the capacity region of the randomized fixed-composition coding is not bigger than the known Han-Kobayashi [24] capacity region. This suggests that the study of the average behavior of randomized codes are not sufficient in finding new capacity regions.

Connected through hyperedge HE-1 (induced by source paper "duality of a source coding problem and the semi deterministic broadcast channel with rate limited cooperation"):
  with members:
    - "empirical coordination in a triangular multiterminal network"
    - "successive refinement with decoder cooperation and its channel coding duals"
    - "evaluation of marton s inner bound for the general broadcast channel"
    - "mac with action dependent state information at one encoder"
    - "the state dependent semideterministic broadcast channel"
    - "multiterminal source coding under logarithmic loss"
    - "the compound multiple access channel with partially cooperating encoders"
    - "compound multiple access channels with partial cooperation"

Connected through hyperedge HE-2 (induced by source paper "random coding error exponents for the two user interference channel"):
  with members:
    - "mismatched multi letter successive decoding for the multiple access channel"
    - "optimal achievable rates for interference networks with random codes"
    - "list decoding random coding exponents and expurgated exponents"
    - "exact correct decoding exponent of the wiretap channel decoder"
    - "exact random coding error exponents of optimal bin index decoding"
    - "erasure list random coding error exponents are not universally achievable"
    - "multiuser random coding techniques for mismatched decoding"
    - "the generalized random energy model and its application to the statistical physics of ensembles of hierarchical codes"

Connected through hyperedge HE-3 (induced by source paper "error exponents of optimum decoding for the interference channel"):
  with members:
    - "a new universal random coding bound for average probability error exponent for multiple access channels"
    - "error exponents for broadcast channels with degraded message sets"
    - "error exponents of erasure list decoding revisited via moments of distance enumerators"
    - "relations between random coding exponents and the statistical physics of random codes"
    - "gaussian interference channel capacity to within one bit"

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)