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 "modulated unit norm tight frames for compressed sensing"):
  Abstract: In this paper, we propose a compressed sensing (CS) framework that consists of three parts: a unit-norm tight frame (UTF), a random diagonal matrix and a column-wise orthonormal matrix. We prove that this structure satisfies the restricted isometry property (RIP) with high probability if the number of measurements m=O(slog 2 slog 2 n) for s-sparse signals of length n and if the column-wise orthonormal matrix is bounded. Some existing structured sensing models can be studied under this framework, which then gives tighter bounds on the required number of measurements to satisfy the RIP. More importantly, we propose several structured sensing models by appealing to this unified framework, such as a general sensing model with arbitrary/determinisic subsamplers, a fast and efficient block compressed sensing scheme, and structured sensing matrices with deterministic phase modulations, all of which can lead to improvements on practical applications. In particular, one of the constructions is applied to simplify the transceiver design of CS-based channel estimation for orthogonal frequency division multiplexing (OFDM) systems.

Includes members:
  Member "sparsity averaging for compressive imaging":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sparse signal recovery via generalized entropy functions minimization")
  Member "convolutional compressed sensing using deterministic sequences":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "swift link a compressive beam alignment algorithm for practical mmwave radios")
  Member "projection design for statistical compressive sensing a tight frame based approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "online multilinear dictionary learning")
  Member "the restricted isometry property for random block diagonal matrices":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sampling of graph signals via randomized local aggregations")
  Member "spread spectrum magnetic resonance imaging":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "do log factors matter on optimal wavelet approximation and the foundations of compressed sensing")
  Member "fast and efficient compressive sensing using structurally random matrices":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sparse signal recovery via generalized entropy functions minimization")
  Member "universal and efficient compressed sensing by spread spectrum and application to realistic fourier imaging techniques":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "uniform recovery bounds for structured random matrices in corrupted compressed sensing")
  Member "a probabilistic and ripless theory of compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "support recovery for sparse signals with unknown non stationary modulation")

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)