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 "convex block sparse linear regression with expanders provably"):
  Abstract: Sparse matrices are favorable objects in machine learning and optimization. When such matrices are used, in place of dense ones, the overall complexity requirements in optimization can be significantly reduced in practice, both in terms of space and run-time. Prompted by this observation, we study a convex optimization scheme for block-sparse recovery from linear measurements. To obtain linear sketches, we use expander matrices, i.e., sparse matrices containing only few non-zeros per column. Hitherto, to the best of our knowledge, such algorithmic solutions have been only studied from a non-convex perspective. Our aim here is to theoretically characterize the performance of convex approaches under such setting. #R##N#Our key novelty is the expression of the recovery error in terms of the model-based norm, while assuring that solution lives in the model. To achieve this, we show that sparse model-based matrices satisfy a group version of the null-space property. Our experimental findings on synthetic and real applications support our claims for faster recovery in the convex setting -- as opposed to using dense sensing matrices, while showing a competitive recovery performance.

Includes members:
  Member "structured sparsity discrete and convex approaches":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimal approximation for unconstrained non submodular minimization")
  Member "group sparse model selection hardness and relaxations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an adaptive sublinear time block sparse fourier transform")
  Member "on model based rip 1 matrices":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stronger l2 l2 compressed sensing without iterating")
  Member "combinatorial selection and least absolute shrinkage via the clash algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "quantitative recovery conditions for tree based compressed sensing")
  Member "on deterministic sketching and streaming for sparse recovery and norm estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on low risk heavy hitters and sparse recovery schemes")
  Member "k median clustering model based compressive sensing and sparse recovery for earth mover distance":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "compressive parameter estimation via k median clustering")
  Member "model based compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "compressed sensing with invertible generative models and dependent noise")
  Member "combining geometry and combinatorics a unified approach to sparse signal recovery":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)