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: "interior point decoding for linear vector channels"
  Abstract: In this paper, a novel decoding algorithm for low-density parity-check (LDPC) codes based on convex optimization is presented. The decoding algorithm, called interior point decoding, is designed for linear vector channels. The linear vector channels include many practically important channels such as inter-symbol interference channels and partial response channels. It is shown that the maximum likelihood decoding (MLD) rule for a linear vector channel can be relaxed to a convex optimization problem, which is called a relaxed MLD problem.

Connected through hyperedge HE-1 (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"
    - "graph based decoding in the presence of isi"
    - "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-2 (induced by source paper "an iterative joint linear programming decoding of ldpc codes and finite state channels"):
  with members:
    - "a unified framework for linear programming based communication receivers"
    - "joint decoding of ldpc codes and finite state channels via linear programming"
    - "low complexity linear programming decoding of nonbinary linear codes"
    - "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-3 (induced by source paper "interior point algorithms for linear programming decoding"):
  with members:
    - "pseudo codeword landscape"
    - "guessing facets polytope structure and improved lp decoder"
    - "convergence of the min sum message passing algorithm for quadratic optimization"
    - "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)