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: "isometric sketching of any set via the restricted isometry property"
  Abstract: In this paper we show that for the purposes of dimensionality reduction certain class of structured random matrices behave similarly to random Gaussian matrices. This class includes several matrices for which matrix-vector multiply can be computed in log-linear time, providing efficient dimensionality reduction of general sets. In particular, we show that using such matrices any set from high dimensions can be embedded into lower dimensions with near optimal distortion. We obtain our results by connecting dimensionality reduction of any set to dimensionality reduction of sparse vectors via a chaining argument.

Connected through hyperedge HE-1 (induced by source paper "quantized compressive sensing with rip matrices the benefit of dithering"):
  with members:
    - "instance optimal decoding and the restricted isometry property"
    - "fast binary embeddings and quantized compressed sensing with structured matrices"
    - "non gaussian hyperplane tessellations and robust one bit compressed sensing"
    - "quantized compressed sensing for partial random circulant matrices"
    - "one bit compressed sensing with partial gaussian circulant matrices"
    - "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing"
    - "optimizing quantization for lasso recovery"
    - "time for dithering fast and quantized random embeddings via the restricted isometry property"

Connected through hyperedge HE-2 (induced by source paper "fast binary embeddings and quantized compressed sensing with structured matrices"):
  with members:
    - "quantized compressed sensing for partial random circulant matrices"
    - "one bit compressed sensing with partial gaussian circulant matrices"
    - "fast binary embeddings with gaussian circulant matrices improved bounds"
    - "improved bounds for sparse recovery from subsampled random convolutions"
    - "distributed noise shaping quantization i beta duals of finite frames and near optimal quantization of random measurements"
    - "time for dithering fast and quantized random embeddings via the restricted isometry property"
    - "from compressed sensing to compressed bit streams practical encoders tractable decoders"
    - "near optimal sample complexity bounds for circulant binary embedding"

Connected through hyperedge HE-3 (induced by source paper "taking the edge off quantization projected back projection in dithered compressive sensing"):
  with members:
    - "quantized compressive sensing with rip matrices the benefit of dithering"
    - "fast binary embeddings and quantized compressed sensing with structured matrices"
    - "one bit compressed sensing with partial gaussian circulant matrices"
    - "optimizing quantization for lasso recovery"
    - "time for dithering fast and quantized random embeddings via the restricted isometry property"
    - "small width low distortions quantized random embeddings of low complexity sets"
    - "the generalized lasso with non linear observations"
    - "near optimal bounds for binary embeddings of arbitrary sets"

Connected through hyperedge HE-4 (induced by source paper "persistent homology for low complexity models"):
  with members:
    - "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"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "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-5 (induced by source paper "time for dithering fast and quantized random embeddings via the restricted isometry property"):
  with members:
    - "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing"
    - "through the haze a non convex approach to blind gain calibration for linear random sensing models"
    - "near optimal sample complexity bounds for circulant binary embedding"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "recipes for stable linear embeddings from hilbert spaces to ℝ m"
    - "self calibration and biconvex compressive sensing"
    - "representation and coding of signal geometry"
    - "small width low distortions quantized random embeddings of low complexity sets"

Connected through hyperedge HE-6 (induced by source paper "sketching meets random projection in the dual a provable recovery algorithm for big and high dimensional data"):
  with members:
    - "doubly stochastic primal dual coordinate method for empirical risk minimization and bilinear saddle point problem"
    - "theory of dual sparse regularized randomized reduction"
    - "sketching as a tool for numerical linear algebra"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "recovering the optimal solution by dual random projection"
    - "improved matrix algorithms via the subsampled randomized hadamard transform"
    - "randomized algorithms for matrices and data"
    - "a simpler approach to matrix completion"

Connected through hyperedge HE-7 (induced by source paper "generalized line spectral estimation via convex optimization"):
  with members:
    - "a theory of super resolution from short time fourier transform measurements"
    - "sharp time data tradeoffs for linear inverse problems"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "super resolution radar"
    - "near minimax line spectral estimation"
    - "atomic norm denoising with applications to line spectral estimation"
    - "a probabilistic and ripless theory of compressed sensing"
    - "new and improved johnson lindenstrauss embeddings via the restricted isometry property"

Connected through hyperedge HE-8 (induced by source paper "near optimal sample complexity bounds for circulant binary embedding"):
  with members:
    - "deep neural networks with random gaussian weights a universal classification strategy"
    - "binary embedding fundamental limits and fast algorithm"
    - "near optimal bounds for binary embeddings of arbitrary sets"
    - "on binary embedding using circulant matrices"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"

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)