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: "the admm penalized decoder for ldpc codes"
  Abstract: Linear programming (LP) decoding for low-density parity-check codes was introduced by Feldman  et al.  and has been shown to have theoretical guarantees in several regimes. Furthermore, it has been reported in the literature—via simulation and via instanton analysis—that LP decoding displays better error rate performance at high signal-to-noise ratios (SNR) than does belief propagation (BP) decoding. However, at low SNRs, LP decoding is observed to have worse performance than BP. In this paper, we seek to improve LP decoding at low SNRs while maintaining LP decoding’s high SNR performance. Our main contribution is a new class of decoders obtained by applying the alternating direction method of multipliers (ADMM) algorithm to a set of non-convex optimization problems. These non-convex problems are constructed by adding a penalty term to the objective of LP decoding. The goal of the penalty is to make pseudocodewords, which are non-integer vertices of the LP relaxation, more costly. We name this class of decoders—ADMM penalized decoders. For low and moderate SNRs, we simulate ADMM penalized decoding with    $\ell _{1}$    and    $\ell _{2}$    penalties. We find that these decoders can outperform both BP and LP decoding. For high SNRs, where it is difficult to obtain data via simulation, we use an instanton analysis and find that, asymptotically, ADMM penalized decoding performs better than BP but not as well as LP. Unfortunately, since ADMM penalized decoding is not a convex program, we have not been successful in developing theoretical guarantees. However, the non-convex program can be approximated using a sequence of linear programs; an approach that yields a reweighted LP decoder. We show that a two-round reweighted LP decoder has an improved theoretical recovery threshold when compared with LP decoding. In addition, we find via simulation that reweighted LP decoding significantly attains lower error rates than LP decoding at low SNRs.

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"
    - "adam a method for stochastic optimization"
    - "deep unfolding model based inspiration of novel deep architectures"
    - "adaptive cut generation algorithm for improved linear programming decoding of binary linear codes"
    - "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"
    - "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"
    - "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:
    - "hardware based linear program decoding with the alternating direction method of multipliers"
    - "hardware based projection onto the parity polytope and probability simplex"
    - "adaptive cut generation algorithm for improved linear programming decoding of binary linear codes"
    - "decomposition methods for large scale lp decoding"
    - "adaptive methods for linear programming decoding"

Connected through hyperedge HE-4 (induced by source paper "deep learning aided trainable projected gradient decoding for ldpc codes"):
  with members:
    - "trainable ista for sparse signal recovery"
    - "learning to decode linear codes using deep learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "minimum polytope based linear programming decoder for ldpc codes via admm approach"):
  with members:
    - "decomposition methods for large scale lp decoding"
    - "adaptive methods for linear programming decoding"

Connected through hyperedge HE-6 (induced by source paper "hardware based admm lp decoding"):
  with members:
    - "hardware based projection onto the parity polytope and probability simplex"
    - "adaptive cut generation algorithm for improved linear programming decoding of binary linear codes"
    - "decomposition methods for large scale lp decoding"
    - "adaptive methods for linear programming decoding"
    - "towards low complexity linear programming decoding"

Connected through hyperedge HE-7 (induced by source paper "lp decodable multipermutation codes"):
  with members:
    - "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"
    - "constructions of rank modulation codes"

Connected through hyperedge HE-8 (induced by source paper "admm lp decoding of non binary ldpc codes in mathbb f _ 2 m"):
  with members:
    - "low complexity lp decoding of nonbinary linear codes"
    - "linear programming decoding of spatially coupled codes"
    - "online alternating direction method"
    - "adaptive cut generation algorithm for improved linear programming decoding of binary linear codes"
    - "decomposition methods for large scale lp decoding"
    - "lp decoding of regular ldpc codes in memoryless channels"
    - "efficient implementation of linear programming decoding"
    - "probabilistic analysis of linear programming decoding"

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)