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: "dimensionality reduction with subgaussian matrices a unified theory"
  Abstract: We present a theory for Euclidean dimensionality reduction with subgaussian matrices which unifies several restricted isometry property and Johnson-Lindenstrauss type results obtained earlier for specific data sets. In particular, we recover and, in several cases, improve results for sets of sparse and structured sparse vectors, low-rank matrices and tensors, and smooth manifolds. In addition, we establish a new Johnson-Lindenstrauss embedding for data sets taking the form of an infinite union of subspaces of a Hilbert space.

Connected through hyperedge HE-1 (induced by source paper "sub gaussian matrices on sets optimal tail dependence and applications"):
  with members:
    - "phase retrieval under a generative prior"
    - "global guarantees for enforcing deep generative priors by empirical risk"
    - "from compressed sensing to compressed bit streams practical encoders tractable decoders"
    - "why are deep nets reversible a simple theory with implications for training"
    - "compressive sensing with redundant dictionaries and structured measurements"
    - "randomized sketches of convex programs with sharp guarantees"
    - "sketching as a tool for numerical linear algebra"
    - "the johnson lindenstrauss lemma is optimal for linear dimensionality reduction"

Connected through hyperedge HE-2 (induced by source paper "phase retrieval of low rank matrices by anchored regression"):
  with members:
    - "blind phaseless short time fourier transform recovery"
    - "blind deconvolutional phase retrieval via convex programming"
    - "blind gain and phase calibration via sparse spectral methods"
    - "solving equations of random convex functions via anchored regression"
    - "blind deconvolution by a steepest descent algorithm on a quotient manifold"
    - "low rank phase retrieval"
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "self calibration via linear least squares"

Connected through hyperedge HE-3 (induced by source paper "compressed subspace learning based on canonical angle preserving property"):
  with members:
    - "johnson lindenstrauss property implies subspace restricted isometry property"
    - "rigorous restricted isometry property of low dimensional subspaces"
    - "restricted isometry property of gaussian random projection for finite set of subspaces"
    - "what happens to a manifold under a bi lipschitz map"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "greedy feature selection for subspace clustering"
    - "the restricted isometry property for random block diagonal matrices"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-4 (induced by source paper "economy statistical recurrent units for inferring nonlinear granger causality"):
  with members:
    - "a convergence theory for deep learning via over parameterization"
    - "the statistical recurrent unit"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-5 (induced by source paper "rigorous restricted isometry property of low dimensional subspaces"):
  with members:
    - "restricted isometry property of gaussian random projection for finite set of subspaces"
    - "what happens to a manifold under a bi lipschitz map"
    - "subspace clustering of dimensionality reduced data"
    - "new analysis of manifold embeddings and signal recovery from compressive measurements"
    - "sparse subspace clustering algorithm theory and applications"
    - "a geometric analysis of subspace clustering with outliers"
    - "information theoretic bounds for compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "optimal terminal dimensionality reduction in euclidean space"):
  with members:
    - "nonlinear dimension reduction via outer bi lipschitz extensions"
    - "optimality of the johnson lindenstrauss lemma"

Connected through hyperedge HE-7 (induced by source paper "persistent homology for low complexity models"):
  with members:
    - "isometric sketching of any set via the restricted isometry property"
    - "practical and optimal lsh for angular distance"
    - "the simplex tree an efficient data structure for general simplicial complexes"
    - "approximation and streaming algorithms for projective clustering via random projections"
    - "the johnson lindenstrauss lemma is optimal for linear dimensionality reduction"
    - "local doubling dimension of point sets"
    - "a unified framework for linear dimensionality reduction in l1"
    - "living on the edge phase transitions in convex programs with random data"

Connected through hyperedge HE-8 (induced by source paper "compressed sensing in hilbert spaces"):
  with members:
    - "sketching for large scale learning of mixture models"
    - "compressive k means"
    - "recipes for stable linear embeddings from hilbert spaces to ℝ m"
    - "stable recovery of low dimensional cones in hilbert spaces one rip to rule them all"
    - "fundamental performance limits for ideal decoders in high dimensional linear inverse problems"
    - "uniform recovery of fusion frame structured sparse signals"
    - "new analysis of manifold embeddings and signal recovery from compressive measurements"
    - "sparse representation of a polytope and recovery of sparse signals and low rank matrices"

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)