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: "theoretic shaping bounds for single letter constraints and mismatched decoding"
  Abstract: Shaping gain is attained in schemes where a shaped subcode is chosen from a larger codebook by a codeword selection process. This includes the popular method of Trellis Shaping (TS), originally proposed by Forney for average power reduction. The decoding process of such schemes is mismatched, since it is aware of only the large codebook. This study models such schemes by a random code construction and derives achievable bounds on the transmission rate under matched and mismatched decoding. For matched decoding the bound is obtained using a modified asymptotic equipartition property (AEP) theorem derived to suit this particular code construction. For mismatched decoding, relying on the large codebook performance is generally wrong, since the performance of the non-typical codewords within the large codebook may differ substantially from the typical ones. Hence, we present two novel lower bounds on the capacity under mismatched decoding. The first is based upon Gallager's random exponent, whereas the second on a modified version of the joint-typicality decoder.

Connected through hyperedge HE-1 (induced by source paper "achievable rates for shaped bit metric decoding"):
  with members:
    - "protograph based ldpc code design for shaped bit metric decoding"
    - "protograph based ldpc code design for bit metric decoding"
    - "probabilistic signal shaping for bit metric decoding"
    - "an efficient algorithm to calculate bicm capacity"
    - "optimal alphabets and binary labelings for bicm at low snr"

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)