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 "another look at expurgated bounds and their statistical mechanical interpretation"):
  Abstract: We revisit the derivation of expurgated error exponents using a method of type class enumeration, which is inspired by statistical-mechanical methods, and which has already been used in the derivation of random coding exponents in several other scenarios. We compare our version of the expurgated bound to both the one by Gallager and the one by Csiszar, Korner and Marton (CKM). For expurgated ensembles of fixed composition codes over finite alphabets, our basic expurgated bound coincides with the CKM expurgated bound, which is in general tighter than Gallager's bound, but with equality for the optimum type class of codewords. Our method, however, extends beyond fixed composition codes and beyond finite alphabets, where it is natural to impose input constraints (e.g., power limitation). In such cases, the CKM expurgated bound may not apply directly, and our bound is in general tighter than Gallager's bound. In addition, while both the CKM and the Gallager expurgated bounds are based on Bhattacharyya bound for bounding the pairwise error probabilities, our bound allows the more general Chernoff distance measure, thus giving rise to additional improvement using the Chernoff parameter as a degree of freedom to be optimized.

Includes members:
  Member "rate distortion function via minimum mean square error estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on mimo transmission with discrete input signals technical challenges advances and future trends")
  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-2 (induced by source paper "random coding error exponents for the two user interference channel")
  Member "error exponents for broadcast channels with degraded message sets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "operational interpretation of renyi information measures via composite hypothesis testing against product and markov distributions")
  Member "error exponents of optimum decoding for the interference channel":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "exact error and erasure exponents for the asymmetric broadcast channel")
  Member "error exponents of erasure list decoding revisited via moments of distance enumerators":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "exact error and erasure exponents for the asymmetric broadcast channel")
  Member "on the statistical physics of directed polymers in a random medium and their relation to tree codes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "zero delay and causal single user and multi user lossy source coding with decoder side information")
  Member "relations between random coding exponents and the statistical physics of random codes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the reliability function of distributed hypothesis testing under optimal detection")

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)