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 "statistical physics of random binning"):
  Abstract: We consider the model of random binning and finite-temperature decoding for Slepian-Wolf codes, from a statistical-mechanical perspective. While ordinary random channel coding is intimately related to the random energy model (REM) - a statistical-mechanical model of disordered magnetic materials, it turns out that random binning (for Slepian-Wolf coding) is analogous to another, related statistical mechanical model of strong disorder, which we call the random dilution model (RDM). We use the latter analogy to characterize phase transitions pertaining to finite- temperature Slepian-Wolf decoding, which are somewhat similar, but not identical, to those of finite-temperature channel decoding. We then provide the exact random coding exponent of the bit error rate (BER) as a function of the coding rate and the decoding temperature, and discuss its properties. Finally, a few modifications and extensions of our results are outlined and discussed.

Includes members:
  Member "exact random coding error exponents of optimal bin index decoding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sharp bounds for mutual covering")
  Member "optimum trade offs between the error exponent and the excess rate exponent of variable rate slepian wolf coding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the reliability function of distributed hypothesis testing under optimal detection")
  Member "erasure list exponents for slepian wolf decoding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gaussian intersymbol interference channels with mismatch")
  Member "improved source coding exponents via witsenhausen s rate":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on hypothesis testing against independence with multiple decision centers")
  Member "relations between random coding exponents and the statistical physics of random codes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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)