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 "finding a sparse vector in a subspace linear sparsity using alternating directions"):
  Abstract: Is it possible to find the sparsest vector (direction) in a generic subspace    $\mathcal {S} \subseteq \mathbb {R} ^{p}$    with    $\text {dim} \left ({ \mathcal {S} }\right )=n   ? This problem can be considered a homogeneous variant of the sparse recovery problem and finds connections to sparse dictionary learning, sparse PCA, and many other problems in signal processing and machine learning. In this paper, we focus on a  planted sparse model  for the subspace: the target sparse vector is embedded in an otherwise random subspace. Simple convex heuristics for this planted recovery problem provably break down when the fraction of nonzero entries in the target sparse vector substantially exceeds    $O(1/\sqrt {n})$   . In contrast, we exhibit a relatively simple nonconvex approach based on alternating directions, which provably succeeds even when the fraction of nonzero entries is    $\Omega (1)$   . To the best of our knowledge, this is the first practical algorithm to achieve linear scaling under the planted sparse model. Empirically, our proposed algorithm also succeeds in more challenging data models, e.g., sparse dictionary learning.

Includes members:
  Member "a geometric analysis of phase retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "simple efficient and neural algorithms for sparse coding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "convolutional dictionary learning based auto encoders for natural exponential family distributions")
  Member "solving random quadratic systems of equations is nearly as easy as solving linear systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "tensor vs matrix methods robust tensor decomposition under block sparse perturbations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "complete dictionary recovery over the sphere i overview and the geometric picture")
  Member "sum of squares lower bounds for sparse pca":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "0 1 phase transitions in sparse spiked matrix estimation")
  Member "speeding up sum of squares for tensor decomposition and planted sparse vectors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the non convex geometry of low rank matrix optimization")
  Member "finding a low rank basis in a matrix subspace":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "robust and resource efficient identification of two hidden layer neural networks")
  Member "dual principal component pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")

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)