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: "capacity achieving linear codes with random binary sparse generating matrices"
  Abstract: In this paper, we prove the existence of capacity achieving linear codes with random binary sparse generating matrices. The results on the existence of capacity achieving linear codes in the literature are limited to the random binary codes with equal probability generating matrix elements and sparse parity-check matrices. Moreover, the codes with sparse generating matrices reported in the literature are not proved to be capacity achieving. #R##N#As opposed to the existing results in the literature, which are based on optimal maximum a posteriori decoders, the proposed approach is based on a different decoder and consequently is suboptimal. We also demonstrate an interesting trade-off between the sparsity of the generating matrix and the error exponent (a constant which determines how exponentially fast the probability of error decays as block length tends to infinity). An interesting observation is that for small block sizes, less sparse generating matrices have better performances while for large blok sizes, the performance of the random generating matrices become independent of the sparsity. Moreover, we prove the existence of capacity achieving linear codes with a given (arbitrarily low) density of ones on rows of the generating matrix. In addition to proving the existence of capacity achieving sparse codes, an important conclusion of our paper is that for a sufficiently large code length, no search is necessary in practice to find a deterministic matrix by proving that any arbitrarily selected sequence of sparse generating matrices is capacity achieving with high probability. The focus in this paper is on the binary symmetric and binary erasure channels.her discrete memory-less symmetric channels.

Connected through hyperedge HE-1 (induced by source paper "update efficiency and local repairability limits for capacity approaching codes"):
  with members:
    - "repairable fountain codes"
    - "optimal locally repairable and secure codes for distributed storage systems"
    - "explicit mbr all symbol locality codes"
    - "an upper bound on the size of locally recoverable codes"
    - "optimal locally repairable codes and connections to matroid theory"
    - "locally repairable codes"
    - "on the locality of codeword symbols"

Connected through hyperedge HE-2 (induced by source paper "rank minimization over finite fields fundamental limits and coding theoretic interpretations"):
  with members:
    - "information theoretic bounds for tensor rank minimization over finite fields"
    - "capacity achieving random sparse linear codes"
    - "information theoretic bounds for low rank matrix completion"
    - "distributed storage codes meet multiple access wiretap channels"
    - "communication over finite field matrix channels"
    - "the power of convex relaxation near optimal matrix completion"
    - "lp decoding meets lp decoding a connection between channel coding and compressed sensing"
    - "guaranteed rank minimization via singular value projection"

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)