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: "recycling randomness with structure for sublinear time kernel expansions"
  Abstract: We propose a scheme for recycling Gaussian random vectors into structured matrices to approximate various kernel functions in sublinear time via random embeddings. Our framework includes the Fastfood construction as a special case, but also extends to Circulant, Toeplitz and Hankel matrices, and the broader family of structured matrices that are characterized by the concept of low-displacement rank. We introduce notions of coherence and graph-theoretic structural constants that control the approximation quality, and prove unbiasedness and low-variance properties of random feature maps that arise within our framework. For the case of low-displacement matrices, we show how the degree of structure and randomness can be controlled to reduce statistical variance at the cost of increased computation and storage requirements. Empirical results strongly support our theory and justify the use of a broader family of structured matrices for scaling up kernel methods using random features.

Connected through hyperedge HE-1 (induced by source paper "on dimension free tail inequalities for sums of random matrices and applications"):
  with members:
    - "matrix infinitely divisible series tail inequalities and applications in optimization"
    - "an exploration of parameter redundancy in deep networks with circulant projections"
    - "a note on element wise matrix sparsification via a matrix valued bernstein inequality"
    - "low rank matrix valued chernoff bounds and approximate matrix multiplication"
    - "circulant and toeplitz matrices in compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "learning compressed transforms with low displacement rank"):
  with members:
    - "learning compact neural networks with regularization"
    - "non parametric transformation networks"
    - "rotation equivariant vector field networks"
    - "geometric deep learning going beyond euclidean data"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"
    - "exploiting cyclic symmetry in convolutional neural networks"
    - "learning compact recurrent neural networks"
    - "group equivariant convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "matrix infinitely divisible series tail inequalities and applications in optimization"):
  with members:
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "an exploration of parameter redundancy in deep networks with circulant projections"
    - "from random matrix theory to coding theory volume of a metric ball in unitary group"
    - "robust estimates of covariance matrices in the large dimensional regime"
    - "improved bounds for the nystrom method with application to kernel classification"
    - "on the theorem of uniform recovery of random sampling matrices"

Connected through hyperedge HE-4 (induced by source paper "quadrature based features for kernel approximation"):
  with members:
    - "gaussian quadrature for kernel features"
    - "on the equivalence between kernel quadrature rules and random feature expansions"
    - "on the error of random fourier features"
    - "fastfood approximate kernel expansions in loglinear time"

Connected through hyperedge HE-5 (induced by source paper "deep structured features for semantic segmentation"):
  with members:
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"
    - "energy efficient machine learning in silicon a communications inspired approach"
    - "discrete deep feature extraction a theory and new architectures"
    - "learning both weights and connections for efficient neural networks"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "a mathematical theory of deep convolutional neural networks for feature extraction"
    - "hypercolumns for object segmentation and fine grained localization"

Connected through hyperedge HE-6 (induced by source paper "sketching for large scale learning of mixture models"):
  with members:
    - "deep neural networks with random gaussian weights a universal classification strategy"
    - "flexible multilayer sparse approximations of matrices and applications"
    - "fastmmd ensemble of circular discrepancy for efficient two sample test"
    - "a la carte learning fast kernels"
    - "fundamental performance limits for ideal decoders in high dimensional linear inverse problems"
    - "learning mixtures of spherical gaussians moment methods and spectral decompositions"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "orthogonal matching pursuit with replacement"

Connected through hyperedge HE-7 (induced by source paper "local group invariant representations via orbit embeddings"):
  with members:
    - "learning from conditional distributions via dual kernel embeddings"
    - "structured adaptive and random spinners for fast machine learning computations"
    - "group equivariant convolutional networks"
    - "learning with group invariant features a kernel perspective"
    - "on invariance and selectivity in representation learning"
    - "scalable kernel methods via doubly stochastic gradients"
    - "how to scale up kernel methods to be as good as deep neural nets"
    - "fastfood approximate kernel expansions in loglinear time"

Connected through hyperedge HE-8 (induced by source paper "structured adaptive and random spinners for fast machine learning computations"):
  with members:
    - "practical and optimal lsh for angular distance"
    - "binary embeddings with structured hashed projections"
    - "randomized sketches of convex programs with sharp guarantees"
    - "scalable kernel methods via doubly stochastic gradients"
    - "convolutional kernel networks"
    - "predicting parameters in deep learning"
    - "a sparse johnson lindenstrauss transform"

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.LG(Machine Learning)