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: "improved bounds on the parity check density and achievable rates of binary linear block codes with applications to ldpc codes"
  Abstract: We derive bounds on the asymptotic density of parity-check matrices and the achievable rates of binary linear block codes transmitted over memoryless binary-input output-symmetric (MBIOS) channels. The lower bounds on the density of arbitrary parity-check matrices are expressed in terms of the gap between the rate of these codes for which reliable communication is achievable and the channel capacity, and the bounds are valid for every sequence of binary linear block codes. These bounds address the question, previously considered by Sason and Urbanke, of how sparse can parity-check matrices of binary linear block codes be as a function of the gap to capacity. Similarly to a previously reported bound by Sason and Urbanke, the new lower bounds on the parity-check density scale like the log of the inverse of the gap to capacity, but their tightness is improved (except for a binary symmetric/erasure channel, where they coincide with the previous bound). The new upper bounds on the achievable rates of binary linear block codes tighten previously reported bounds by Burshtein et al., and therefore enable to obtain tighter upper bounds on the thresholds of sequences of binary linear block codes under ML decoding. The bounds are applied to lowdensity parity-check (LDPC) codes, and the improvement in their tightness is exemplifled numerically. The upper bounds on the achievable rates enable to assess the inherent loss in performance of various iterative decoding algorithms as compared to optimal ML decoding. The lower bounds on the asymptotic parity-check density are helpful in assessing the inherent tradeofi between the asymptotic performance of LDPC codes and their decoding complexity (per iteration) under message-passing decoding.

Connected through hyperedge HE-1 (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"
    - "accumulate repeat accumulate codes systematic codes achieving the binary erasure channel capacity with bounded complexity"
    - "capacity achieving codes with bounded graphical complexity on noisy channels"
    - "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-2 (induced by source paper "the generalized area theorem and some of its consequences"):
  with members:
    - "maxwell construction the hidden bridge between iterative and maximum a posteriori decoding"
    - "tight bounds for ldpc and ldgm codes under map decoding"
    - "an mse based ttransfer chart to analyze iterative decoding schemes"
    - "life above threshold from list decoding to area theorem and mse"
    - "mutual information and minimum mean square error in gaussian channels"

Connected through hyperedge HE-3 (induced by source paper "on the parity check density and achievable rates of ldpc codes"):
  with members:
    - "tight bounds for ldpc and ldgm codes under map decoding"
    - "capacity achieving ensembles for the binary erasure channel with bounded complexity"
    - "life above threshold from list decoding to area theorem and mse"

Connected through hyperedge HE-4 (induced by source paper "on achievable rates and complexity of ldpc codes for parallel channels information theoretic bounds and applications"):
  with members:
    - "on achievable rates and complexity of ldpc codes for parallel channels with application to puncturing"
    - "capacity achieving ensembles for the binary erasure channel with bounded complexity"

Connected through hyperedge HE-5 (induced by source paper "on achievable rates and complexity of ldpc codes for parallel channels with application to puncturing"):
  with members:
    - "maxwell construction the hidden bridge between iterative and maximum a posteriori decoding"
    - "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)