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 "universal decoding for source channel coding with side information"):
  Abstract: We consider a setting of Slepian--Wolf coding, where the random bin of the source vector undergoes channel coding, and then decoded at the receiver, based on additional side information, correlated to the source. For a given distribution of the randomly selected channel codewords, we propose a universal decoder that depends on the statistics of neither the correlated sources nor the channel, assuming first that they are both memoryless. Exact analysis of the random-binning/random-coding error exponent of this universal decoder shows that it is the same as the one achieved by the optimal maximum a-posteriori (MAP) decoder. Previously known results on universal Slepian-Wolf source decoding, universal channel decoding, and universal source-channel decoding, are all obtained as special cases of this result. Subsequently, we further generalize the results in several directions, including: (i) finite-state sources and finite-state channels, along with a universal decoding metric that is based on Lempel-Ziv parsing, (ii) arbitrary sources and channels, where the universal decoding is with respect to a given class of decoding metrics, and (iii) full (symmetric) Slepian-Wolf coding, where both source streams are separately fed into random-binning source encoders, followed by random channel encoders, which are then jointly decoded by a universal decoder.

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 "erasure list exponents for slepian wolf decoding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "on the bee identification error exponent with absentee bees")
  Member "multiuser random coding techniques for mismatched decoding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "strategic communication with side information at the decoder")
  Member "communication over individual channels a general framework":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gaussian intersymbol interference channels with mismatch")
  Member "the porosity of additive noise sequences":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robust gaussian joint source channel coding with a staircase distortion noise profile")
  Member "communication over individual channels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "robust gaussian joint source channel coding with a staircase distortion noise profile")
  Member "universal communication part i modulo additive channels":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a universal decoder relative to a given family of metrics")

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)