human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "approximation of points on low dimensional manifolds via random linear projections"
  Abstract: This paper considers the approximate reconstruction of points, x \in R^D, which are close to a given compact d-dimensional submanifold, M, of R^D using a small number of linear measurements of x. In particular, it is shown that a number of measurements of x which is independent of the extrinsic dimension D suffices for highly accurate reconstruction of a given x with high probability. Furthermore, it is also proven that all vectors, x, which are sufficiently close to M can be reconstructed with uniform approximation guarantees when the number of linear measurements of x depends logarithmically on D. Finally, the proofs of these facts are constructive: A practical algorithm for manifold-based signal recovery is presented in the process of proving the two main results mentioned above.

Connected through hyperedge HE-1 (induced by source paper "new algorithms and improved guarantees for one bit compressed sensing on manifolds"):
  with members:
    - "fast binary embeddings and quantized compressed sensing with structured matrices"
    - "on recovery guarantees for one bit compressed sensing on manifolds"
    - "quantization for low rank matrix recovery"
    - "distributed noise shaping quantization i beta duals of finite frames and near optimal quantization of random measurements"
    - "from compressed sensing to compressed bit streams practical encoders tractable decoders"
    - "quantization and compressive sensing"
    - "one bit compressed sensing by linear programming"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"

Connected through hyperedge HE-2 (induced by source paper "restricted isometry property of subspace projection matrix under random compression"):
  with members:
    - "new analysis of manifold embeddings and signal recovery from compressive measurements"
    - "stable manifold embeddings with structured random matrices"
    - "online identification and tracking of subspaces from highly incomplete information"

Connected through hyperedge HE-3 (induced by source paper "what happens to a manifold under a bi lipschitz map"):
  with members:
    - "new analysis of manifold embeddings and signal recovery from compressive measurements"
    - "stable manifold embeddings with structured random matrices"

Connected through hyperedge HE-4 (induced by source paper "new analysis of manifold embeddings and signal recovery from compressive measurements"):
  with members:
    - "matched filtering from limited frequency samples"
    - "the restricted isometry property for random block diagonal matrices"
    - "signal recovery on incoherent manifolds"
    - "stable manifold embeddings with structured random matrices"
    - "measurement bounds for sparse signal ensembles via graphical models"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "restricted isometries for partial random circulant matrices"
    - "circulant and toeplitz matrices in compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "a compressed sensing framework for magnetic resonance fingerprinting"):
  with members:
    - "compressive source separation theory and methods for hyperspectral imaging"
    - "on variable density compressive sampling"
    - "sampling and reconstructing signals from a union of linear subspaces"
    - "iterative hard thresholding for compressed sensing"

Question: Please tell me which class the center node 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)