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 "edge coloring and stopping sets analysis in product codes with mds components"):
  Abstract: We consider non-binary product codes with MDS components and their iterative row-column algebraic decoding on the erasure channel. Both independent and block erasures are considered in this paper. A compact graph representation is introduced on which we define double-diversity edge colorings via the rootcheck concept. An upper bound of the number of decoding iterations is given as a function of the graph size and the color palette size $M$. Stopping sets are defined in the context of MDS components and a relationship is established with the graph representation. A full characterization of these stopping sets is given up to a size $(d_1+1)(d_2+1)$, where $d_1$ and $d_2$ are the minimum Hamming distances of the column and row MDS components respectively. Then, we propose a differential evolution edge coloring algorithm that produces colorings with a large population of minimal rootcheck order symbols. The complexity of this algorithm per iteration is $o(M^{\aleph})$, for a given differential evolution parameter $\aleph$, where $M^{\aleph}$ itself is small with respect to the huge cardinality of the coloring ensemble. The performance of MDS-based product codes with and without double-diversity coloring is analyzed in presence of both block and independent erasures. In the latter case, ML and iterative decoding are proven to coincide at small channel erasure probability. Furthermore, numerical results show excellent performance in presence of unequal erasure probability due to double-diversity colorings.

Includes members:
  Member "a bound on rate of codes with locality with sequential recovery from multiple erasures":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rate optimal binary linear locally repairable codes with small availability")
  Member "reed muller codes achieve capacity on erasure channels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "partially symmetric monomial codes")
  Member "reed muller codes achieve capacity on the binary erasure channel under map decoding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "repairing generalized reed muller codes")
  Member "codes with locality for two erasures":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "new bounds on the field size for maximally recoverable codes instantiating grid like topologies")
  Member "a solution to the network challenges of data recovery in erasure coded distributed storage systems a study on the facebook warehouse cluster":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "convertible codes efficient conversion of coded data in distributed storage")
  Member "irregular product codes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "practical product code construction of polar codes")
  Member "spatially coupled ensembles universally achieve capacity under belief propagation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "on the locality of codeword symbols":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a locality based approach for coded computation")

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)