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: "on achievable rates for channels with mismatched decoding"
  Abstract: The problem of mismatched decoding for discrete memoryless channels is addressed. A mismatched cognitive multiple-access channel is introduced, and an inner bound on its capacity region is derived using two alternative encoding methods: superposition coding and random binning. The inner bounds are derived by analyzing the average error probability of the code ensemble for both methods and by a tight characterization of the resulting error exponents. Random coding converse theorems are also derived. A comparison of the achievable regions shows that in the matched case, random binning performs as well as superposition coding, i.e., the region achievable by random binning is equal to the capacity region. The achievability results are further specialized to obtain a lower bound on the mismatch capacity of the single-user channel by investigating a cognitive multiple access channel whose achievable sum-rate serves as a lower bound on the single-user channel's capacity. In certain cases, for given auxiliary random variables this bound strictly improves on the achievable rate derived by Lapidoth.

Connected through hyperedge HE-1 (induced by source paper "mismatched multi letter successive decoding for the multiple access channel"):
  with members:
    - "random coding error exponents for the two user interference channel"
    - "re proving channel polarization theorems an extremality and robustness analysis"
    - "polarization as a novel architecture to boost the classical mismatched capacity of b dmcs"
    - "a technique for deriving one shot achievability results in network information theory"
    - "multiuser coding techniques for mismatched decoding"
    - "mismatched decoding error exponents second order rates and saddlepoint approximations"
    - "a general formula for the mismatch capacity"
    - "multiuser random coding techniques for mismatched decoding"

Connected through hyperedge HE-2 (induced by source paper "mismatched decoding error exponents second order rates and saddlepoint approximations"):
  with members:
    - "expurgated random coding ensembles exponents refinements and connections"
    - "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"
    - "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-3 (induced by source paper "a general formula for the mismatch capacity"):
  with members:
    - "expurgated random coding ensembles exponents refinements and connections"
    - "the zero undetected error capacity approaches the sperner capacity"
    - "mismatched decoding error exponents second order rates and saddlepoint approximations"
    - "a new outer bound and the noisy interference sum rate capacity for gaussian interference channels"
    - "capacity bounds for the gaussian interference channel"

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)