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: "adaptive cut generation algorithm for improved linear programming decoding of binary linear codes"
  Abstract: Linear programming (LP) decoding approximates maximum-likelihood (ML) decoding of a linear block code by relaxing the equivalent ML integer programming (IP) problem into a more easily solved LP problem. The LP problem is defined by a set of box constraints together with a set of linear inequalities called “parity inequalities” that are derived from the constraints represented by the rows of a parity-check matrix of the code and can be added iteratively and adaptively. In this paper, we first derive a new necessary condition and a new sufficient condition for a violated parity inequality constraint, or “cut,” at a point in the unit hypercube. Then, we propose a new and effective algorithm to generate parity inequalities derived from certain additional redundant parity check (RPC) constraints that can eliminate pseudocodewords produced by the LP decoder, often significantly improving the decoder error-rate performance. The cut-generating algorithm is based upon a specific transformation of an initial parity-check matrix of the linear block code. We also design two variations of the proposed decoder to make it more efficient when it is combined with the new cut-generating algorithm. Simulation results for several low-density parity-check (LDPC) codes demonstrate that the proposed decoding algorithms significantly narrow the performance gap between LP decoding and ML decoding.

Connected through hyperedge HE-1 (induced by source paper "admm based decoder for binary linear codes aided by deep learning"):
  with members:
    - "deep learning methods for improved decoding of linear codes"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "the admm penalized decoder for ldpc codes"
    - "adam a method for stochastic optimization"
    - "deep unfolding model based inspiration of novel deep architectures"
    - "decomposition methods for large scale lp decoding"

Connected through hyperedge HE-2 (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"
    - "improved linear programming decoding of ldpc codes and bounds on the minimum and fractional distance"
    - "efficient linear programming decoding of hdpc codes"
    - "a separation algorithm for improved lp decoding of linear block codes"

Connected through hyperedge HE-3 (induced by source paper "a reduced complexity projection algorithm for admm based lp decoding"):
  with members:
    - "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"
    - "decomposition methods for large scale lp decoding"
    - "adaptive methods for linear programming decoding"

Connected through hyperedge HE-4 (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-5 (induced by source paper "a branch price and cut algorithm for optimal decoding of ldpc codes"):
  with members:
    - "nash codes for noisy channels"
    - "decomposition methods for large scale lp decoding"
    - "budget optimal task allocation for reliable crowdsourcing systems"
    - "a separation algorithm for improved lp decoding of linear block codes"
    - "adaptive methods for linear programming decoding"

Connected through hyperedge HE-6 (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-7 (induced by source paper "adaptive linear programming decoding of nonbinary linear codes over prime fields"):
  with members:
    - "hardware based linear program decoding with the alternating direction method of multipliers"
    - "on the finite length scaling of ternary polar codes"
    - "low complexity lp decoding of nonbinary linear codes"
    - "mathematical programming decoding of binary linear codes theory and algorithms"
    - "decomposition methods for large scale lp decoding"
    - "efficient implementation of linear programming decoding"
    - "linear programming decoding of nonbinary linear codes"
    - "a separation algorithm for improved lp decoding of linear block codes"

Connected through hyperedge HE-8 (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"
    - "decomposition methods for large scale lp decoding"
    - "instantons causing iterative decoding to cycle"

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)