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 "lp decodable multipermutation codes"):
  Abstract: In this paper, we introduce a new way of constructing and decoding multipermutation codes. Multipermutations are permutations of a multiset that generally consist of duplicate entries. We first introduce a class of binary matrices called multipermutation matrices, each of which corresponds to a unique and distinct multipermutation. By enforcing a set of linear constraints on these matrices, we define a new class of codes that we term LP-decodable multipermutation codes. In order to decode these codes using a linear program (LP), thereby enabling soft decoding, we characterize the convex hull of multipermutation matrices. This characterization allows us to relax the coding constraints to a polytope and to derive two LP decoding problems. These two problems are respectively formulated by relaxing the maximum likelihood decoding problem and the minimum Chebyshev distance decoding problem. #R##N#Because these codes are non-linear, we also study efficient encoding and decoding algorithms. We first describe an algorithm that maps consecutive integers, one by one, to an ordered list of multipermutations. Based on this algorithm, we develop an encoding algorithm for a code proposed by Shieh and Tsai, a code that falls into our class of LP-decodable multipermutation codes. Regarding decoding algorithms, we propose an efficient distributed decoding algorithm based on the alternating direction method of multipliers (ADMM). Finally, we observe from simulation results that the soft decoding techniques we introduce can significantly outperform hard decoding techniques that are based on quantized channel outputs.

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 "admm lp decoding of non binary ldpc codes in mathbb f _ 2 m":
    also connected through other hyperedges:
      (none)
  Member "multipermutation codes in the ulam metric for nonvolatile memories":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "locality and availability in distributed storage")
  Member "systematic error correcting codes for rank modulation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimal constant weight codes under l_1 metric")
  Member "error correction in flash memories via codes in the ulam metric":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimal constant weight codes under l_1 metric")
  Member "decomposition methods for large scale lp decoding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "admm based decoder for binary linear codes aided by deep learning")
  Member "coded modulation for power line communications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ulam sphere size analysis for permutation and multipermutation codes correcting translocation errors")
  Member "snake in the box codes for rank modulation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "infinity norm permutation covering codes from cyclic groups")

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)