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 "sparsification of matrices and compressed sensing"):
  Abstract: Compressed sensing is a signal processing technique whereby the limits imposed by the Shannon--Nyquist theorem can be exceeded provided certain conditions are imposed on the signal. Such conditions occur in many real-world scenarios, and compressed sensing has emerging applications in medical imaging, big data, and statistics. Finding practical matrix constructions and computationally efficient recovery algorithms for compressed sensing is an area of intense research interest. Many probabilistic matrix constructions have been proposed, and it is now well known that matrices with entries drawn from a suitable probability distribution are essentially optimal for compressed sensing. #R##N#Potential applications have motivated the search for constructions of sparse compressed sensing matrices (i.e., matrices containing few non-zero entries). Various constructions have been proposed, and simulations suggest that their performance is comparable to that of dense matrices. In this paper, extensive simulations are presented which suggest that sparsification leads to a marked improvement in compressed sensing performance for a large class of matrix constructions and for many different recovery algorithms.

Includes members:
  Member "deterministic constructions of binary measurement matrices from finite geometry":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deterministic construction of binary measurement matrices with flexible sizes")
  Member "near optimal binary compressed sensing matrix":
    also connected through other hyperedges:
      (none)
  Member "ldpc codes for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unknown sparsity in compressed sensing denoising and inference")
  Member "sparsity lower bounds for dimensionality reducing maps":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "privacy preserving learning via deep net pruning")
  Member "fast and efficient compressive sensing using structurally random matrices":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sparse signal recovery via generalized entropy functions minimization")
  Member "vanishingly sparse matrices and expander graphs with application to compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the construction of sparse matrices from expander graphs")
  Member "restricted isometries for partial random circulant matrices":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "random quadratic forms with dependence applications to restricted isometry and beyond")
  Member "performance bounds for expander based compressed sensing in the presence of poisson noise":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "compressed sensing performance bounds under poisson noise")

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)