human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "distance distribution of binary codes and the error probability of decoding"
  Abstract: We address the problem of bounding below the probability of error under maximum likelihood decoding of a binary code with a known distance distribution used on a binary symmetric channel. An improved upper bound is given for the maximum attainable exponent of this probability (the reliability function of the channel). In particular, we prove that the ``random coding exponent'' is the true value of the channel reliability for code rate $R$ in some interval immediately below the critical rate of the channel. An analogous result is obtained for the Gaussian channel.

Connected through hyperedge HE-1 (induced by source paper "adaptive causal network coding with feedback"):
  with members:
    - "resource allocation and harq optimization for urllc traffic in 5g wireless networks"
    - "tiny codes for guaranteeable delay"
    - "complete mdp convolutional codes"
    - "throughput maximization for delay sensitive random access communication"
    - "analysis of feedback error in automatic repeat request"
    - "low delay random linear coding over a stream"
    - "the effect of block wise feedback on the throughput delay trade off in streaming"
    - "an elias bound on the bhattacharyya distance of codes for channels with a zero error capacity"

Connected through hyperedge HE-2 (induced by source paper "on the bee identification error exponent with absentee bees"):
  with members:
    - "capacity results for the noisy shuffling channel"
    - "on identifying a massive number of distributions"
    - "codes in the space of multisets coding for permutation channels with impairments"
    - "fundamental limits of dna storage systems"
    - "universal decoding for arbitrary channels relative to a given class of decoding metrics"

Connected through hyperedge HE-3 (induced by source paper "an information theoretic characterization of channels that die"):
  with members:
    - "refinement of the sphere packing bound asymmetric channels"
    - "channels that heat up"
    - "the capacity of channels with feedback"
    - "intermediate performance of rateless codes"

Connected through hyperedge HE-4 (induced by source paper "diversity order vs rate in an awgn channel"):
  with members:
    - "code spectrum and reliability function gaussian channel"
    - "an improved sphere packing bound for finite length codes on symmetric memoryless channels"

Question: Please tell me which class the center node 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)