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: "efficient linear programming decoding of hdpc codes"
  Abstract: We propose several improvements for Linear Programming (LP) decoding algorithms for High Density Parity Check (HDPC) codes. First, we use the automorphism groups of a code to create parity check matrix diversity and to generate valid cuts from redundant parity checks. Second, we propose an efficient mixed integer decoder utilizing the branch and bound method. We further enhance the proposed decoders by removing inactive constraints and by adapting the parity check matrix prior to decoding according to the channel observations. Based on simulation results the proposed decoders achieve near-ML performance with reasonable complexity.

Connected through hyperedge HE-1 (induced by source paper "efficient qp admm decoder for binary ldpc codes and its performance analysis"):
  with members:
    - "minimum polytope based linear programming decoder for ldpc codes via admm approach"
    - "the admm penalized decoder for ldpc codes"
    - "hardware based linear program decoding with the alternating direction method of multipliers"
    - "hardware based projection onto the parity polytope and probability simplex"
    - "mathematical programming decoding of binary linear codes theory and algorithms"
    - "adaptive cut generation algorithm for improved linear programming decoding of binary linear codes"
    - "improved linear programming decoding of ldpc codes and bounds on the minimum and fractional distance"
    - "a separation algorithm for improved lp decoding of linear block 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 "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-4 (induced by source paper "the admm penalized decoder for ldpc codes"):
  with members:
    - "hierarchical and high girth qc ldpc codes"
    - "linear programming decoding of spatially coupled codes"
    - "an improved three weight message passing algorithm"
    - "counting in graph covers a combinatorial characterization of the bethe entropy function"
    - "mathematical programming decoding of binary linear codes theory and algorithms"
    - "error floor approximation for ldpc codes in the awgn channel"
    - "adaptive cut generation algorithm for improved linear programming decoding of binary linear codes"
    - "decomposition methods for large scale lp decoding"

Connected through hyperedge HE-5 (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"
    - "iterative soft input soft output decoding of reed solomon codes by adapting the parity check matrix"

Connected through hyperedge HE-6 (induced by source paper "lp decodable multipermutation codes"):
  with members:
    - "the admm penalized decoder for ldpc codes"
    - "admm lp decoding of non binary ldpc codes in mathbb f _ 2 m"
    - "multipermutation codes in the ulam metric for nonvolatile memories"
    - "systematic error correcting codes for rank modulation"
    - "error correction in flash memories via codes in the ulam metric"
    - "decomposition methods for large scale lp decoding"
    - "coded modulation for power line communications"
    - "snake in the box codes for rank modulation"

Connected through hyperedge HE-7 (induced by source paper "mathematical programming decoding of binary linear codes theory and algorithms"):
  with members:
    - "polytope of correct linear programming decoding and low weight pseudo codewords"
    - "error bounds for repeat accumulate codes decoded via linear programming"
    - "efficient implementation of linear programming decoding"
    - "a decomposition theory for binary linear codes"
    - "max product for maximum weight matching convergence correctness and lp duality"
    - "interior point algorithms for linear programming decoding"
    - "a cutting plane method based on redundant rows for improving fractional distance"
    - "linear programming decoding of nonbinary linear codes"

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)