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: "stable manifold embeddings with structured random matrices"
  Abstract: The fields of compressed sensing (CS) and matrix completion have shown that high-dimensional signals with sparse or low-rank structure can be effectively projected into a low-dimensional space (for efficient acquisition or processing) when the projection operator achieves a stable embedding of the data by satisfying the Restricted Isometry Property (RIP). It has also been shown that such stable embeddings can be achieved for general Riemannian submanifolds when random orthoprojectors are used for dimensionality reduction. Due to computational costs and system constraints, the CS community has recently explored the RIP for structured random matrices (e.g., random convolutions, localized measurements, deterministic constructions). The main contribution of this paper is to show that any matrix satisfying the RIP (i.e., providing a stable embedding for sparse signals) can be used to construct a stable embedding for manifold-modeled signals by randomizing the column signs and paying reasonable additional factors in the number of measurements. We demonstrate this result with several new constructions for stable manifold embeddings using structured matrices. This result allows advances in efficient projection schemes for sparse signals to be immediately applied to manifold signal models.

Connected through hyperedge HE-1 (induced by source paper "quickly finding the best linear model in high dimensions"):
  with members:
    - "quantized compressive sensing with rip matrices the benefit of dithering"
    - "stochastic gradient descent learns state equations with nonlinear activations"
    - "learning compact neural networks with regularization"
    - "robust one bit compressed sensing with non gaussian measurements"
    - "non gaussian hyperplane tessellations and robust one bit compressed sensing"
    - "robust one bit compressed sensing with partial circulant matrices"
    - "the generalized lasso for sub gaussian measurements with dithered quantization"
    - "one bit compressed sensing with partial gaussian circulant matrices"

Connected through hyperedge HE-2 (induced by source paper "restricted isometry property of gaussian random projection for finite set of subspaces"):
  with members:
    - "subspace clustering of dimensionality reduced data"
    - "new analysis of manifold embeddings and signal recovery from compressive measurements"
    - "achievable angles between two compressed sparse vectors under norm distance constraints imposed by the restricted isometry property a plane geometry approach"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"
    - "a geometric analysis of subspace clustering with outliers"
    - "structured compressed sensing from theory to applications"
    - "block sparse signals uncertainty relations and efficient recovery"

Connected through hyperedge HE-3 (induced by source paper "isometric sketching of any set via the restricted isometry property"):
  with members:
    - "the restricted isometry property of subsampled fourier matrices"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "sharp time data tradeoffs for linear inverse problems"
    - "learning without concentration"
    - "randomized sketches of convex programs with sharp guarantees"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "fast and rip optimal transforms"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"

Connected through hyperedge HE-4 (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"
    - "approximation of points on low dimensional manifolds via random linear projections"
    - "online identification and tracking of subspaces from highly incomplete information"

Connected through hyperedge HE-5 (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"
    - "approximation of points on low dimensional manifolds via random linear projections"

Connected through hyperedge HE-6 (induced by source paper "compressed subspace matching on the continuum"):
  with members:
    - "compressive parameter estimation for sparse translation invariant signals using polar interpolation"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "compressive parameter estimation in awgn"
    - "matched filtering from limited frequency samples"
    - "near minimax line spectral estimation"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"
    - "blind deconvolution using convex programming"
    - "sparse recovery from combined fusion frame measurements"

Connected through hyperedge HE-7 (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"
    - "approximation of points on low dimensional manifolds via random linear projections"
    - "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-8 (induced by source paper "stable embedding of grassmann manifold via gaussian random matrices"):
  with members:
    - "new analysis of manifold embeddings and signal recovery from compressive measurements"
    - "achievable angles between two compressed sparse vectors under norm distance constraints imposed by the restricted isometry property a plane geometry approach"
    - "structured compressed sensing from theory to applications"
    - "time delay estimation from low rate samples a union of subspaces approach"
    - "model based compressive sensing"
    - "information theoretic bounds for compressed sensing"
    - "quantization bounds on grassmann manifolds and applications to mimo communications"

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)