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 "relations between random coding exponents and the statistical physics of random codes"):
  Abstract: The partition function pertaining to finite--temperature decoding of a (typical) randomly chosen code is known to have three types of behavior, corresponding to three phases in the plane of rate vs. temperature: the {\it ferromagnetic phase}, corresponding to correct decoding, the {\it paramagnetic phase}, of complete disorder, which is dominated by exponentially many incorrect codewords, and the {\it glassy phase} (or the condensed phase), where the system is frozen at minimum energy and dominated by subexponentially many incorrect codewords. We show that the statistical physics associated with the two latter phases are intimately related to random coding exponents. In particular, the exponent associated with the probability of correct decoding at rates above capacity is directly related to the free energy in the glassy phase, and the exponent associated with probability of error (the error exponent) at rates below capacity, is strongly related to the free energy in the paramagnetic phase. In fact, we derive alternative expressions of these exponents in terms of the corresponding free energies, and make an attempt to obtain some insights from these expressions. Finally, as a side result, we also compare the phase diagram associated with a simple finite-temperature universal decoder for discrete memoryless channels, to that of the finite--temperature decoder that is aware of the channel statistics.

Includes members:
  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-1 (induced by source paper "random coding error exponents for the two user interference channel")
  Member "error exponents of optimum decoding for the interference channel":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "exact error and erasure exponents for the asymmetric broadcast channel")
  Member "modern coding theory the statistical mechanics and computer science point of view":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on universal properties of capacity approaching ldpc code ensembles")
  Member "a statistical mechanical interpretation of instantaneous codes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a statistical mechanical interpretation of algorithmic information theory")
  Member "capacity of complexity constrained noise free cdma":
    also connected through other hyperedges:
      (none)

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)