human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "random coding error exponents for the two user interference channel"):
  Abstract: This paper is about deriving lower bounds on the error exponents for the two-user interference channel under the random coding regime for several ensembles. Specifically, we first analyze the standard random coding ensemble, where the codebooks are comprised of independently and identically distributed (i.i.d.) codewords. For this ensemble, we focus on optimum decoding, which is in contrast to other, suboptimal decoding rules that have been used in the literature (e.g., joint typicality decoding, treating interference as noise, etc.). The fact that the interfering signal is a codeword, rather than an i.i.d. noise process, complicates the application of conventional techniques of performance analysis of the optimum decoder. Also, unfortunately, these conventional techniques result in loose bounds. Using analytical tools rooted in statistical physics, as well as advanced union bounds, we derive single-letter formulas for the random coding error exponents. We compare our results with the best known lower bound on the error exponent, and show that our exponents can be strictly better. Then, in the second part of this paper, we consider more complicated coding ensembles, and find a lower bound on the error exponent associated with the celebrated Han-Kobayashi (HK) random coding ensemble, which is based on superposition coding.

Includes members:
  Member "mismatched multi letter successive decoding for the multiple access channel":
    also connected through other hyperedges:
      (none)
  Member "optimal achievable rates for interference networks with random codes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "performance of multi cell massive mimo systems with interference decoding")
  Member "list decoding random coding exponents and expurgated exponents":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mismatched guesswork")
  Member "exact correct decoding exponent of the wiretap channel decoder":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exact random coding error exponents of optimal bin index decoding")
  Member "exact random coding error exponents of optimal bin index decoding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sharp bounds for mutual covering")
  Member "erasure list random coding error exponents are not universally achievable":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mismatched guesswork")
  Member "multiuser random coding techniques for mismatched decoding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "strategic communication with side information at the decoder")
  Member "the generalized random energy model and its application to the statistical physics of ensembles of hierarchical codes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "another look at expurgated bounds and their statistical mechanical interpretation")

Question: Please tell me which class the source paper 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)