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 "summary based structures with improved sublinear recovery for compressed sensing"):
  Abstract: We introduce a new class of measurement matrices for compressed sensing, using low order summaries over binary sequences of a given length. We prove recovery guarantees for three reconstruction algorithms using the proposed measurements, including $\ell_1$ minimization and two combinatorial methods. In particular, one of the algorithms recovers $k$-sparse vectors of length $N$ in sublinear time $\text{poly}(k\log{N})$, and requires at most $\Omega(k\log{N}\log\log{N})$ measurements. The empirical oversampling constant of the algorithm is significantly better than existing sublinear recovery algorithms such as Chaining Pursuit and Sudocodes. In particular, for $10^3\leq N\leq 10^8$ and $k=100$, the oversampling factor is between 3 to 8. We provide preliminary insight into how the proposed constructions, and the fast recovery scheme can be used in a number of practical applications such as market basket analysis, and real time compressed sensing implementation.

Includes members:
  Member "structured sublinear compressive sensing via belief propagation":
    also connected through other hyperedges:
      (none)
  Member "compressed sensing how sharp is the restricted isometry property":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "limits on sparse data acquisition ric analysis of finite gaussian matrices")
  Member "from theory to practice sub nyquist sampling of sparse wideband analog signals":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "image guided depth sampling and reconstruction")
  Member "compressed sensing of analog signals in shift invariant spaces":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sharp sufficient conditions for stable recovery of block sparse signals by block orthogonal matching pursuit")
  Member "sparse recovery of positive signals with minimal expansion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "perfect recovery conditions for non negative sparse modeling")
  Member "combining geometry and combinatorics a unified approach to sparse signal recovery":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sublinear time non adaptive group testing with o k log n tests via bit mixing coding")

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)