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: "iterative soft input soft output decoding of reed solomon codes by adapting the parity check matrix"
  Abstract: An iterative algorithm is presented for soft-input-soft-output (SISO) decoding of Reed-Solomon (RS) codes. The proposed iterative algorithm uses the sum product algorithm (SPA) in conjunction with a binary parity check matrix of the RS code. The novelty is in reducing a submatrix of the binary parity check matrix that corresponds to less reliable bits to a sparse nature before the SPA is applied at each iteration. The proposed algorithm can be geometrically interpreted as a two-stage gradient descent with an adaptive potential function. This adaptive procedure is crucial to the convergence behavior of the gradient descent algorithm and, therefore, significantly improves the performance. Simulation results show that the proposed decoding algorithm and its variations provide significant gain over hard decision decoding (HDD) and compare favorably with other popular soft decision decoding methods.

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"
    - "multiple bases belief propagation decoding of high density cyclic codes"

Connected through hyperedge HE-2 (induced by source paper "deep learning methods for improved decoding of linear codes"):
  with members:
    - "an introduction to deep learning for the physical layer"
    - "performance bounds of concatenated polar coding schemes"
    - "scaling deep learning based decoding of polar codes via partitioning"
    - "rnn decoding of linear block codes"
    - "detection algorithms for communication systems using deep learning"
    - "neural offset min sum decoding"
    - "on deep learning based channel decoding"
    - "learning to decode linear codes using deep learning"

Connected through hyperedge HE-3 (induced by source paper "learning from the syndrome"):
  with members:
    - "online label recovery for deep learning based communication through error correcting codes"
    - "deep learning methods for improved decoding of linear codes"
    - "deep learning for decoding of linear codes a syndrome based approach"
    - "scaling deep learning based decoding of polar codes via partitioning"
    - "rnn decoding of linear block codes"
    - "neural offset min sum decoding"
    - "learning to decode linear codes using deep learning"
    - "adam a method for stochastic optimization"

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"
    - "multiple bases belief propagation decoding of high density cyclic codes"
    - "adaptive methods for linear programming decoding"

Connected through hyperedge HE-5 (induced by source paper "rnn decoding of linear block codes"):
  with members:
    - "an introduction to deep learning for the physical layer"
    - "neural offset min sum decoding"
    - "on deep learning based channel decoding"
    - "learning to decode linear codes using deep learning"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "effective approaches to attention based neural machine translation"
    - "deep residual learning for image recognition"
    - "efficient maximum likelihood decoding of linear block codes on binary memoryless channels"

Connected through hyperedge HE-6 (induced by source paper "a study on the impact of locality in the decoding of binary cyclic codes"):
  with members:
    - "combinatorial alphabet dependent bounds for locally recoverable codes"
    - "constructions of optimal cyclic r delta locally repairable codes"
    - "cyclic lrc codes binary lrc codes and upper bounds on the distance of cyclic codes"
    - "codes with hierarchical locality"
    - "cyclic lrc codes and their subfield subcodes"
    - "bounds on the parameters of locally recoverable codes"
    - "locally recoverable codes on algebraic curves"
    - "repair locality with multiple erasure tolerance"

Connected through hyperedge HE-7 (induced by source paper "learning to decode linear codes using deep learning"):
  with members:
    - "effective approaches to attention based neural machine translation"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "efficient maximum likelihood decoding of linear block codes on binary memoryless channels"
    - "speech recognition with deep recurrent neural networks"
    - "adadelta an adaptive learning rate method"
    - "adaptive cut generation algorithm for improved linear programming decoding of binary linear codes"
    - "efficient linear programming decoding of hdpc codes"

Connected through hyperedge HE-8 (induced by source paper "bounds on the ml decoding error probability of rs coded modulation over awgn channels"):
  with members:
    - "new techniques for upper bounding the ml decoding performance of binary linear codes"
    - "tightened upper bounds on the ml decoding error probability of binary linear block codes"
    - "maximum likelihood decoding of reed solomon codes is np hard"

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)