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 "restricted isometry property of gaussian random projection for finite set of subspaces"):
  Abstract: Dimension reduction plays an essential role when decreasing the complexity of solving large-scale problems. The well-known Johnson–Lindenstrauss (JL) lemma and restricted isometry property (RIP) admit the use of random projection to reduce the dimension while keeping the Euclidean distance, which leads to the boom of compressed sensing and the field of sparsity related signal processing. Recently, successful applications of sparse models in computer vision and machine learning have increasingly hinted that the underlying structure of high dimensional data looks more like a union of subspaces. In this paper, motivated by JL lemma and an emerging field of compressed subspace clustering, we study for the first time the RIP of Gaussian random matrices for the compression of two subspaces based on the generalized projection   $F$  -norm distance. We theoretically prove that with high probability the  affinity  or  distance  between two projected subspaces are concentrated around their estimates. When the ambient dimension after projection is sufficiently large, the affinity and distance between two subspaces almost remain unchanged after random projection. Numerical experiments verify the theoretical work.

Includes members:
  Member "subspace clustering of dimensionality reduced data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rigorous restricted isometry property of low dimensional subspaces")
  Member "new analysis of manifold embeddings and signal recovery from compressive measurements":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fast and provable admm for learning with generative priors")
  Member "achievable angles between two compressed sparse vectors under norm distance constraints imposed by the restricted isometry property a plane geometry approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fast binary embedding via circulant downsampled matrix a data independent approach")
  Member "robust recovery of subspace structures by low rank representation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gated fusion network for degraded image super resolution")
  Member "sparse subspace clustering algorithm theory and applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "provably efficient exploration for rl with unsupervised learning")
  Member "a geometric analysis of subspace clustering with outliers":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "arae adversarially robust training of autoencoders improves novelty detection")
  Member "stable manifold embeddings with structured random matrices":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "quickly finding the best linear model in high dimensions")
  Member "structured compressed sensing from theory to applications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "serial quantization for sparse time sequences")

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)