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: "graph based decoding in the presence of isi"
  Abstract: We propose a new graph representation for ISI channels that can be used for combined equalization and decoding by linear programming (LP) or iterative message-passing (IMP) decoding algorithms. We derive this graph representation by linearizing the ML detection metric, which transforms the equalization problem into a classical decoding problem. We observe that the performance of LP and IMP decoding on this model are very similar in the uncoded case, while IMP decoding significantly outperforms LP decoding when low-density parity-check (LDPC) codes are used. In particular, in the absence of coding, for certain classes of channels, both LP and IMP algorithms always find the exact ML solution using the proposed graph representation, without complexity that is exponential in the size of the channel memory. This applies even to certain two-dimensional ISI channels. However, for some other channel impulse responses, both decoders have nondiminishing probability of failure as SNR increases. We provide analytical explanations for many of these observations. In addition, we study the error events of LP decoding in the uncoded case, and derive a measure that can be used to classify ISI channels in terms of the performance of the proposed detection scheme.

Connected through hyperedge HE-1 (induced by source paper "design of a unified transport triggered processor for ldpc turbo decoder"):
  with members:
    - "design of a transport triggered architecture processor for flexible iterative turbo decoder"

Connected through hyperedge HE-2 (induced by source paper "a unified framework for linear programming based communication receivers"):
  with members:
    - "joint decoding of ldpc codes and finite state channels via linear programming"
    - "interior point decoding for linear vector channels"
    - "interior point algorithms for linear programming decoding"
    - "linear programming decoding of nonbinary linear codes"
    - "codeword independent performance of nonbinary linear codes under linear programming and sum product decoding"
    - "towards low complexity linear programming decoding"
    - "pseudocodeword weights for non binary ldpc codes"
    - "characterizations of pseudo codewords of ldpc codes"

Connected through hyperedge HE-3 (induced by source paper "joint decoding of ldpc codes and finite state channels via linear programming"):
  with members:
    - "a unified framework for linear programming based communication receivers"
    - "an iterative joint linear programming decoding of ldpc codes and finite state channels"
    - "low complexity linear programming decoding of nonbinary linear codes"
    - "linear programming receivers"
    - "interior point algorithms for linear programming decoding"
    - "linear programming decoding of nonbinary linear codes"
    - "adaptive methods for linear programming decoding"
    - "towards low complexity linear programming decoding"

Connected through hyperedge HE-4 (induced by source paper "linear programming receivers"):
  with members:
    - "linear programming decoding of nonbinary linear codes"
    - "characterizations of pseudo codewords of ldpc codes"
    - "graph cover decoding and finite length analysis of message passing iterative decoding of ldpc codes"

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)