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: "decoding reed muller codes using minimum weight parity checks"
  Abstract: Reed-Muller (RM) codes exhibit good performance under maximum-likelihood (ML) decoding due to their highly-symmetric structure. In this paper, we explore the question of whether the code symmetry of RM codes can also be exploited to achieve near-ML performance in practice. The main idea is to apply iterative decoding to a highly-redundant parity-check (PC) matrix that contains only the minimum-weight dual codewords as rows. As examples, we consider the peeling decoder for the binary erasure channel, linear-programming and belief propagation (BP) decoding for the binary-input additive white Gaussian noise channel, and bit-flipping and BP decoding for the binary symmetric channel. For short block lengths, it is shown that near-ML performance can indeed be achieved in many cases. We also propose a method to tailor the PC matrix to the received observation by selecting only a small fraction of useful minimum-weight PCs before decoding begins. This allows one to both improve performance and significantly reduce complexity compared to using the full set of minimum-weight PCs.

Connected through hyperedge HE-1 (induced by source paper "reed muller codes theory and algorithms"):
  with members:
    - "reed muller codes polarize"
    - "recursive projection aggregation decoding of reed muller codes"
    - "general strong polarization"
    - "on the performance of reed muller codes with respect to random errors and erasures"
    - "on the bias of reed muller codes over odd prime fields"
    - "almost optimal scaling of reed muller codes on bec and bsc channels"
    - "soft decision decoding of reed muller codes recursive lists"
    - "syndrome decoding of reed muller codes and tensor decomposition over finite fields"

Connected through hyperedge HE-2 (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"
    - "learning to decode linear codes using deep learning"
    - "adam a method for stochastic optimization"
    - "multiple bases belief propagation decoding of high density cyclic codes"
    - "iterative soft input soft output decoding of reed solomon codes by adapting the parity check matrix"

Connected through hyperedge HE-3 (induced by source paper "on decoding using codewords of the dual code"):
  with members:
    - "list decoding of polar codes"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"

Connected through hyperedge HE-4 (induced by source paper "reed muller codes polarize"):
  with members:
    - "recursive projection aggregation decoding of reed muller codes"
    - "general strong polarization"
    - "on the performance of reed muller codes with respect to random errors and erasures"
    - "almost optimal scaling of reed muller codes on bec and bsc channels"
    - "recursive decoding and its performance for low rate reed muller codes"
    - "algebraic properties of polar codes from a new polynomial formalism"
    - "high girth matrices and polarization"
    - "reed muller codes achieve capacity on erasure channels"

Connected through hyperedge HE-5 (induced by source paper "recursive projection aggregation decoding of reed muller codes"):
  with members:
    - "reed muller codes theory and algorithms"
    - "reed muller codes polarize"
    - "on the performance of reed muller codes with respect to random errors and erasures"
    - "decoding reed muller and polar codes by successive factor graph permutations"
    - "almost optimal scaling of reed muller codes on bec and bsc channels"
    - "soft decision decoding of reed muller codes recursive lists"
    - "recursive decoding and its performance for low rate reed muller codes"
    - "fast list decoders for polar codes"

Connected through hyperedge HE-6 (induced by source paper "reinforcement learning for channel coding learned bit flipping decoding"):
  with members:
    - "learning to flip successive cancellation decoding of polar codes with lstm networks"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "deep learning for decoding of linear codes a syndrome based approach"
    - "an introduction to deep learning for the physical layer"
    - "soft decision decoding of reed muller codes recursive lists"
    - "on deep learning based channel decoding"
    - "learning to decode linear codes using deep learning"
    - "residual belief propagation informed scheduling for asynchronous message passing"

Connected through hyperedge HE-7 (induced by source paper "learned belief propagation decoding with simple scaling and snr adaptation"):
  with members:
    - "what can machine learning teach us about communications"
    - "deep learning methods for improved decoding of linear codes"
    - "deep learning for decoding of linear codes a syndrome based approach"
    - "learning to decode linear codes using deep learning"

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)