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: "multiple bases belief propagation decoding of high density cyclic codes"
  Abstract: We introduce a new method for decoding short and moderate-length linear block codes with dense parity check matrix representations of cyclic form. This approach is termed multiple-bases belief-propagation. The proposed iterative scheme makes use of the fact that a code has many structurally diverse parity-check matrices, capable of detecting different error patterns. We show that this inherent code property leads to decoding algorithms with significantly better performance when compared to standard belief-propagation decoding. Furthermore, we describe how to choose sets of parity-check matrices of cyclic form amenable for multiple-bases decoding, based on analytical studies performed for the binary erasure channel. For several cyclic and extended cyclic codes, the multiple-bases belief propagation decoding performance can be shown to closely follow that of the maximum-likelihood decoder.

Connected through hyperedge HE-1 (induced by source paper "pruning neural belief propagation decoders"):
  with members:
    - "learned belief propagation decoding with simple scaling and snr adaptation"
    - "deep learning methods for improved decoding of linear codes"
    - "decoding reed muller codes using minimum weight parity checks"
    - "learning to decode linear codes using deep learning"
    - "adam a method for stochastic optimization"
    - "iterative soft input soft output decoding of reed solomon codes by adapting the parity check matrix"

Connected through hyperedge HE-2 (induced by source paper "what can machine learning teach us about communications"):
  with members:
    - "deep learning methods for improved decoding of linear codes"
    - "wideband time domain digital backpropagation via subband processing and deep learning"
    - "deep learning for decoding of linear codes a syndrome based approach"
    - "asic implementation of time domain digital backpropagation with deep learned chromatic dispersion filters"
    - "nonlinear interference mitigation via deep neural networks"
    - "learning to decode linear codes using deep learning"

Connected through hyperedge HE-3 (induced by source paper "efficient error correcting codes in the short blocklength regime"):
  with members:
    - "low complexity joint channel estimation and list decoding of short codes"
    - "low latency short packet transmissions fixed length or harq"
    - "design of polar codes in 5g new radio"
    - "joint design of convolutional code and crc under serial list viterbi decoding"
    - "on the error probability of short concatenated polar and cyclic codes with interleaving"
    - "short packets over block memoryless fading channels pilot assisted or noncoherent transmission"
    - "polar code construction for list decoding"
    - "ultra sparse non binary ldpc codes for probabilistic amplitude shaping"

Connected through hyperedge HE-4 (induced by source paper "decoding reed muller codes using minimum weight parity checks"):
  with members:
    - "code design for short blocks a survey"
    - "performance comparison of short length error correcting codes"
    - "list decoding of polar codes"
    - "decomposition methods for large scale lp decoding"
    - "adaptive methods for linear programming decoding"
    - "iterative soft input soft output decoding of reed solomon codes by adapting the parity check matrix"

Connected through hyperedge HE-5 (induced by source paper "new two stage automorphism group decoders for cyclic codes in the erasure channel"):
  with members:
    - "stopping sets of algebraic geometry codes"
    - "optimal exact regenerating codes for distributed storage at the msr and mbr points via a product matrix construction"
    - "single exclusion number and the stopping redundancy of mds codes"
    - "permutation decoding and the stopping redundancy hierarchy of cyclic and extended cyclic codes"
    - "on parity check collections for iterative erasure decoding that correct all correctable erasure patterns of a given size"
    - "on the stopping distance and the stopping redundancy of codes"

Connected through hyperedge HE-6 (induced by source paper "code design for short blocks a survey"):
  with members:
    - "coded random access applying codes on graphs to design random access protocols"
    - "reliability output decoding of tail biting convolutional codes"
    - "advanced channel coding for space mission telecommand links"
    - "five disruptive technology directions for 5g"
    - "list decoding of polar codes"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "accumulate repeat accumulate codes capacity achieving ensembles of systematic codes for the erasure channel with bounded complexity"
    - "channel coding the road to channel capacity"

Connected through hyperedge HE-7 (induced by source paper "structured sublinear compressive sensing via belief propagation"):
  with members:
    - "sparse reconstruction via the reed muller sieve"
    - "construction of a large class of deterministic sensing matrices that satisfy a statistical isometry property"
    - "lp decoding meets lp decoding a connection between channel coding and compressed sensing"
    - "message passing algorithms for compressed sensing"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "bayesian compressive sensing via belief propagation"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"
    - "encoding for the blackwell channel with reinforced belief propagation"

Connected through hyperedge HE-8 (induced by source paper "moderate density parity check codes"):
  with members:
    - "iterative decoding on multiple tanner graphs using random edge local complementation"
    - "channel coding the road to channel capacity"

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)