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 "a universal decoder relative to a given family of metrics"):
  Abstract: Consider the following framework of universal decoding suggested in [MerhavUniversal]. Given a family of decoding metrics and random coding distribution (prior), a single, universal, decoder is optimal if for any possible channel the average error probability when using this decoder is better than the error probability attained by the best decoder in the family up to a subexponential multiplicative factor. We describe a general universal decoder in this framework. The penalty for using this universal decoder is computed. The universal metric is constructed as follows. For each metric, a canonical metric is defined and conditions for the given prior to be normal are given. A sub-exponential set of canonical metrics of normal prior can be merged to a single universal optimal metric. We provide an example where this decoder is optimal while the decoder of [MerhavUniversal] is not.

Includes members:
  Member "universal decoding for gaussian intersymbol interference channels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gaussian intersymbol interference channels with mismatch")
  Member "universal decoding for arbitrary channels relative to a given class of decoding metrics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the bee identification error exponent with absentee bees")
  Member "communication over individual channels a general framework":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gaussian intersymbol interference channels with mismatch")
  Member "universal communication over arbitrarily varying channels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning an adversary s actions for secret communication")
  Member "universal communication part i modulo additive channels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "universal decoding for source channel coding with side information")
  Member "blind decoding of linear gaussian channels with isi capacity error exponent universality":
    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)