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: "accumulate repeat accumulate codes systematic codes achieving the binary erasure channel capacity with bounded complexity"
  Abstract: The paper introduces ensembles of accumulate-repeat-accumulate (ARA) codes which asymptotically achieve capacity on the binary erasure channel (BEC) with {\em bounded complexity} per information bit. It also introduces symmetry properties which play a central role in the construction of capacity-achieving ensembles for the BEC. The results here improve on the tradeoff between performance and complexity provided by the first capacity-achieving ensembles of irregular repeat-accumulate (IRA) codes with bounded complexity per information bit; these IRA ensembles were previously constructed by Pfister, Sason and Urbanke. The superiority of ARA codes with moderate to large block length is exemplified by computer simulations which compare their performance with those of previously reported capacity-achieving ensembles of LDPC and IRA codes. The ARA codes also have the advantage of being systematic.

Connected through hyperedge HE-1 (induced by source paper "tradeoff between decoding complexity and rate for codes on graphs"):
  with members:
    - "capacity achieving codes with bounded graphical complexity on noisy channels"
    - "capacity achieving ensembles for the binary erasure channel with bounded complexity"

Connected through hyperedge HE-2 (induced by source paper "performance versus complexity per iteration for low density parity check codes an information theoretic approach"):
  with members:
    - "maxwell construction the hidden bridge between iterative and maximum a posteriori decoding"
    - "capacity achieving codes with bounded graphical complexity on noisy channels"
    - "improved bounds on the parity check density and achievable rates of binary linear block codes with applications to ldpc codes"
    - "on achievable rates and complexity of ldpc codes for parallel channels with application to puncturing"
    - "why we can not surpass capacity the matching condition"
    - "capacity achieving ensembles for the binary erasure channel with bounded complexity"
    - "life above threshold from list decoding to area theorem and mse"
    - "bounds on the decoding complexity of punctured codes on graphs"

Connected through hyperedge HE-3 (induced by source paper "capacity achieving codes with bounded graphical complexity on noisy channels"):
  with members:
    - "capacity achieving ensembles for the binary erasure channel with bounded complexity"

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)