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 subspace projection matrix under random compression"):
  Abstract: Structures play a significant role in the field of signal processing. As a representative of structural data, low rank matrix along with its restricted isometry property (RIP) has been an important research topic in compressive signal processing. Subspace projection matrix is a kind of low rank matrix with additional structure, which allows for further reduction of its intrinsic dimension. This leaves room for improving its own RIP, which could work as the foundation of compressed subspace projection matrix recovery. In this work, we study the RIP of subspace projection matrix under random orthonormal compression. Considering the fact that subspace projection matrices of   $s$   dimensional subspaces in   ${\BBR^N}$    form an   $s(N - s)$   dimensional submanifold in   ${\BBR^{N \times N}}$   , our main concern is transformed to the stable embedding of such submanifold into   ${\BBR^{N \times N}}$  . The result is that by   $O(s(N - s)\log N)$   number of random measurements the RIP of subspace projection matrix is guaranteed.

Includes members:
  Member "new analysis of manifold embeddings and signal recovery from compressive measurements":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fast and provable admm for learning with generative priors")
  Member "stable manifold embeddings with structured random matrices":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "quickly finding the best linear model in high dimensions")
  Member "approximation of points on low dimensional manifolds via random linear projections":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "new algorithms and improved guarantees for one bit compressed sensing on manifolds")
  Member "online identification and tracking of subspaces from highly incomplete information":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "online high rank matrix completion")

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)