human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "admm lp decoding of non binary ldpc codes in mathbb f _ 2 m"):
  Abstract: In this paper, we develop efficient decoders for non-binary low-density parity-check (LDPC) codes using the alternating direction method of multipliers (ADMM). We apply ADMM to two decoding problems. The first problem is linear programming (LP) decoding. In order to develop an efficient algorithm, we focus on non-binary codes in fields of characteristic two. This allows us to transform each constraint in $\mathbb{F}_{2^m}$ to a set of constraints in $\mathbb{F}_{2}$ that has a factor graph representation. Applying ADMM to the LP decoding problem results in two types of non-trivial sub-routines. The first type requires us to solve an unconstrained quadratic program. We solve this problem efficiently by leveraging new results obtained from studying the above factor graphs. The second type requires Euclidean projection onto polytopes that are studied in the literature, a projection that can be solved efficiently using off-the-shelf techniques, which scale linearly in the dimension of the vector to project. ADMM LP decoding scales linearly with block length, linearly with check degree, and quadratically with field size. The second problem we consider is a penalized LP decoding problem. This problem is obtained by incorporating a penalty term into the LP decoding objective. The purpose of the penalty term is to make non-integer solutions (pseudocodewords) more expensive and hence to improve decoding performance. The ADMM algorithm for the penalized LP problem requires Euclidean projection onto a polytope formed by embedding the constraints specified by the non-binary single parity-check code, which can be solved by applying the ADMM technique to the resulting quadratic program. Empirically, this decoder achieves a much reduced error rate than LP decoding at low signal-to-noise ratios.

Includes members:
  Member "the admm penalized decoder for ldpc codes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "admm based decoder for binary linear codes aided by deep learning")
  Member "low complexity lp decoding of nonbinary linear codes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adaptive linear programming decoding of nonbinary linear codes over prime fields")
  Member "linear programming decoding of spatially coupled codes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the admm penalized decoder for ldpc codes")
  Member "online alternating direction method":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "online learning a comprehensive survey")
  Member "adaptive cut generation algorithm for improved linear programming decoding of binary linear codes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "admm based decoder for binary linear codes aided by deep learning")
  Member "decomposition methods for large scale lp decoding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "admm based decoder for binary linear codes aided by deep learning")
  Member "lp decoding of regular ldpc codes in memoryless channels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the admm penalized decoder for ldpc codes")
  Member "efficient implementation of linear programming decoding":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adaptive linear programming decoding of nonbinary linear codes over prime fields")

Question: Please tell me which class the source paper 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)