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 "learning based compressive subsampling"):
  Abstract: The problem of recovering a structured signal   $\text{x} \in {\mathbb C}^p$   from a set of dimensionality-reduced linear measurements   $\text{b} = \text{Ax}$   arises in a variety of applications, such as medical imaging, spectroscopy, Fourier optics, and computerized tomography. Due to computational and storage complexity or physical constraints imposed by the problem, the measurement matrix   $\text{A} \in {\mathbb C}^{n \times p}$   is often of the form   $\text{A} = \text{P}_{\Omega}{{\Psi}}$   for some orthonormal basis matrix   ${{\Psi}}\in {\mathbb C}^{p \times p}$   and subsampling operator   $\text{P}_{\Omega}: {\mathbb C}^{p} \rightarrow {\mathbb C}^{n}$   that selects the rows indexed by   $\Omega$  . This raises the fundamental question of how best to choose the index set   $\Omega$   in order to optimize the recovery performance. Previous approaches to addressing this question rely on nonuniform  random  subsampling using application-specific knowledge of the structure of   $\text{x}$  . In this paper, we instead take a principled learning-based approach in which a  fixed  index set is chosen based on a set of training signals   $\text{x}_1,\ldots,\text{x}_m$  . We formulate combinatorial optimization problems seeking to maximize the energy captured in these signals in an average-case or worst-case sense, and we show that these can be efficiently solved either exactly or approximately via the identification of modularity and submodularity structures. We provide both deterministic and statistical theoretical guarantees showing how the resulting measurement matrices perform on signals differing from the training signals, and we provide numerical examples showing our approach to be effective on a variety of data sets.

Includes members:
  Member "a totally unimodular view of structured sparsity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimal approximation for unconstrained non submodular minimization")
  Member "breaking the coherence barrier a new theory for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the troublesome kernel why deep learning for inverse problems is typically unstable")
  Member "group sparse model selection hardness and relaxations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "an adaptive sublinear time block sparse fourier transform")
  Member "an algorithm for variable density sampling with block constrained acquisition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on oracle type local recovery guarantees in compressed sensing")
  Member "energy aware adaptive bi lipschitz embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "representation and coding of signal geometry")
  Member "a tight combinatorial algorithm for submodular maximization subject to a matroid constraint":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximability of monotone submodular function maximization under cardinality and matroid constraints in the streaming model")
  Member "combinatorial selection and least absolute shrinkage via the clash algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "quantitative recovery conditions for tree based compressed sensing")
  Member "on variable density compressive sampling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "close encounters of the binary kind signal reconstruction guarantees for compressive hadamard sampling with haar wavelet basis")

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)