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: "expurgated random coding ensembles exponents refinements and connections"
  Abstract: Reference EPFL-ARTICLE-203538 URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6810903 Record created on 2014-11-25, modified on 2017-05-10

Connected through hyperedge HE-1 (induced by source paper "ratio list decoding"):
  with members:
    - "converse theorems for the dmc with mismatched decoding"
    - "list decoding random coding exponents and expurgated exponents"
    - "mismatched decoding error exponents second order rates and saddlepoint approximations"
    - "a general formula for the mismatch capacity"
    - "list decoding of polar codes"

Connected through hyperedge HE-2 (induced by source paper "generalized random gilbert varshamov codes"):
  with members:
    - "the generalized stochastic likelihood decoder random coding and expurgated bounds"
    - "list decoding random coding exponents and expurgated exponents"
    - "mismatched decoding error exponents second order rates and saddlepoint approximations"

Connected through hyperedge HE-3 (induced by source paper "simplified erasure list decoding"):
  with members:
    - "asymmetric evaluations of erasure and undetected error probabilities"
    - "list decoding random coding exponents and expurgated exponents"
    - "erasure list random coding error exponents are not universally achievable"
    - "erasure list exponents for slepian wolf decoding"
    - "a neyman pearson approach to universal erasure and list decoding"

Connected through hyperedge HE-4 (induced by source paper "list decoding random coding exponents and expurgated exponents"):
  with members:
    - "asymptotically optimal decision rules for joint detection and source coding"
    - "codeword or noise exact random coding exponents for slotted asynchronism"

Connected through hyperedge HE-5 (induced by source paper "mismatched decoding error exponents second order rates and saddlepoint approximations"):
  with members:
    - "multiuser coding techniques for mismatched decoding"
    - "a general formula for the mismatch capacity"
    - "refinement of the random coding bound"
    - "a derivation of the asymptotic random coding prefactor"
    - "on achievable rates for channels with mismatched decoding"
    - "moderate deviations in channel coding"
    - "refinement of the sphere packing bound asymmetric channels"
    - "error exponent for multiple access channels lower bounds"

Connected through hyperedge HE-6 (induced by source paper "a general formula for the mismatch capacity"):
  with members:
    - "the zero undetected error capacity approaches the sperner capacity"
    - "mismatched decoding error exponents second order rates and saddlepoint approximations"
    - "on achievable rates for channels with mismatched decoding"
    - "a new outer bound and the noisy interference sum rate capacity for gaussian interference channels"
    - "capacity bounds for the gaussian interference channel"

Connected through hyperedge HE-7 (induced by source paper "multiuser random coding techniques for mismatched decoding"):
  with members:
    - "on the reliability function of the discrete memoryless relay channel"
    - "a counter example to the mismatched decoding converse for binary input discrete memoryless channels"
    - "mismatched decoding error exponents second order rates and saddlepoint approximations"
    - "error exponent for multiple access channels lower bounds"

Connected through hyperedge HE-8 (induced by source paper "a derivation of the asymptotic random coding prefactor"):
  with members:
    - "refinement of the random coding bound"

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)