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: "universal communication part i modulo additive channels"
  Abstract: Which communication rates can be attained over a channel whose output is an unknown (possibly stochastic) function of the input that may vary arbitrarily in time with no a-priori model? Following the spirit of the finite-state compressibility of a sequence, defined by Lempel and Ziv, a "capacity" is defined for such a channel as the highest rate achievable by a designer knowing the particular relation that indeed exists between the input and output for all times, yet is constrained to use a fixed finite-length block communication scheme without feedback, i.e. use the same encoder and decoder over each block. In the case of the modulo additive channel, where the output sequence is obtained by modulo addition of an unknown individual sequence to the input sequence, this capacity is upper bounded by a function of the finite state compressibility of the noise sequence. A universal communication scheme with feedback that attains this capacity universally, without prior knowledge of the noise sequence, is presented.

Connected through hyperedge HE-1 (induced by source paper "universal decoding for source channel coding with side information"):
  with members:
    - "exact random coding error exponents of optimal bin index decoding"
    - "erasure list exponents for slepian wolf decoding"
    - "universal decoding for arbitrary channels relative to a given class of decoding metrics"
    - "multiuser random coding techniques for mismatched decoding"
    - "communication over individual channels a general framework"
    - "the porosity of additive noise sequences"
    - "communication over individual channels"
    - "relations between random coding exponents and the statistical physics of random codes"

Connected through hyperedge HE-2 (induced by source paper "a universal decoder relative to a given family of metrics"):
  with members:
    - "universal decoding for gaussian intersymbol interference channels"
    - "universal decoding for arbitrary channels relative to a given class of decoding metrics"
    - "communication over individual channels a general framework"
    - "universal communication over arbitrarily varying channels"
    - "blind decoding of linear gaussian channels with isi capacity error exponent universality"

Connected through hyperedge HE-3 (induced by source paper "universal decoding for gaussian intersymbol interference channels"):
  with members:
    - "asymptotic mmse analysis under sparse representation modeling"
    - "analysis of mismatched estimation errors using gradients of partition functions"
    - "universal decoding for arbitrary channels relative to a given class of decoding metrics"
    - "communication over individual channels a general framework"
    - "the porosity of additive noise sequences"

Connected through hyperedge HE-4 (induced by source paper "universal decoding for arbitrary channels relative to a given class of decoding metrics"):
  with members:
    - "communication over individual channels a general framework"
    - "the porosity of additive noise sequences"

Connected through hyperedge HE-5 (induced by source paper "universal communication part ii channels with memory"):
  with members:
    - "the porosity of additive noise sequences"
    - "universal communication over arbitrarily varying channels"
    - "communication over individual channels"
    - "zero rate feedback can achieve the empirical capacity"

Connected through hyperedge HE-6 (induced by source paper "communication over individual channels a general framework"):
  with members:
    - "communication over individual channels"
    - "an achievable rate for the mimo individual channel"
    - "zero rate feedback can achieve the empirical capacity"

Connected through hyperedge HE-7 (induced by source paper "universal communication over arbitrarily varying channels"):
  with members:
    - "communication over individual channels"
    - "zero rate feedback can achieve the empirical capacity"
    - "achieving the empirical capacity using feedback part i memoryless additive models"

Connected through hyperedge HE-8 (induced by source paper "perfectly secure encryption of individual sequences"):
  with members:
    - "efficient on line schemes for encoding individual sequences with side information at the decoder"
    - "on the wyner ziv problem for individual sequences"
    - "on context tree prediction of individual sequences"

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)