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 "rank minimization over finite fields fundamental limits and coding theoretic interpretations"):
  Abstract: This paper establishes information-theoretic limits for estimating a finite-field low-rank matrix given random linear measurements of it. These linear measurements are obtained by taking inner products of the low-rank matrix with random sensing matrices. Necessary and sufficient conditions on the number of measurements required are provided. It is shown that these conditions are sharp and the minimum-rank decoder is asymptotically optimal. The reliability function of this decoder is also derived by appealing to de Caen's lower bound on the probability of a union. The sufficient condition also holds when the sensing matrices are sparse-a scenario that may be amenable to efficient decoding. More precisely, it is shown that if the n × n-sensing matrices contain, on average, Ω(nlog n) entries, the number of measurements required is the same as that when the sensing matrices are dense and contain entries drawn uniformly at random from the field. Analogies are drawn between the aforementioned results and rank-metric codes in the coding theory literature. In fact, we are also strongly motivated by understanding when minimum rank distance decoding of random rank-metric codes succeeds. To this end, we derive minimum distance properties of equiprobable and sparse rank-metric codes. These distance properties provide a precise geometric interpretation of the fact that the sparse ensemble requires as few measurements as the dense one.

Includes members:
  Member "information theoretic bounds for tensor rank minimization over finite fields":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on identity testing of tensors low rank recovery and compressed sensing")
  Member "capacity achieving linear codes with random binary sparse generating matrices":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "update efficiency and local repairability limits for capacity approaching codes")
  Member "capacity achieving random sparse linear codes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "error correcting codes for distributed control")
  Member "information theoretic bounds for low rank matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "haplotype assembly an information theoretic view")
  Member "distributed storage codes meet multiple access wiretap channels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mimo multiple access channel with an arbitrarily varying eavesdropper")
  Member "communication over finite field matrix channels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "finite field matrix channels for network coding")
  Member "the power of convex relaxation near optimal matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ell_0 motivated low rank sparse subspace clustering")
  Member "lp decoding meets lp decoding a connection between channel coding and compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "bi level protected compressive sampling")

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)