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: "fastmmd ensemble of circular discrepancy for efficient two sample test"
  Abstract: The maximum mean discrepancy MMD is a recently proposed test statistic for the two-sample test. Its quadratic time complexity, however, greatly hampers its availability to large-scale applications. To accelerate the MMD calculation, in this study we propose an efficient method called FastMMD. The core idea of FastMMD is to equivalently transform the MMD with shift-invariant kernels into the amplitude expectation of a linear combination of sinusoid components based on Bochner's theorem and Fourier transform Rahimi & Recht, 2007. Taking advantage of sampling the Fourier transform, FastMMD decreases the time complexity for MMD calculation from     to    , where N and d are the size and dimension of the sample set, respectively. Here, L is the number of basis functions for approximating kernels that determines the approximation accuracy. For kernels that are spherically invariant, the computation can be further accelerated to     by using the Fastfood technique Le, Sarlos, & Smola, 2013. The uniform convergence of our method has also been theoretically proved in both unbiased and biased estimates. We also provide a geometric explanation for our method, ensemble of circular discrepancy, which helps us understand the insight of MMD and we hope will lead to more extensive metrics for assessing the two-sample test task. Experimental results substantiate that the accuracy of FastMMD is similar to that of MMD and with faster computation and lower variance than existing MMD approximation methods.

Connected through hyperedge HE-1 (induced by source paper "the gaussiansketch for almost relative error kernel distance"):
  with members:
    - "hashing based estimators for kernel density in high dimensions"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "locality sensitive hashing of curves"
    - "sublinear time low rank approximation of positive semidefinite matrices"
    - "is input sparsity time possible for kernel low rank approximation"
    - "optimality of the johnson lindenstrauss lemma"
    - "streaming kernel principal component analysis"
    - "sketching as a tool for numerical linear algebra"

Connected through hyperedge HE-2 (induced by source paper "learning implicit generative models with the method of learned moments"):
  with members:
    - "on convergence and stability of gans"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "mcgan mean and covariance feature matching gan"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "mmd gan towards deeper understanding of moment matching network"
    - "coulomb gans provably optimal nash equilibria via potential fields"
    - "unrolled generative adversarial networks"
    - "improved techniques for training gans"

Connected through hyperedge HE-3 (induced by source paper "sketching for large scale learning of mixture models"):
  with members:
    - "recycling randomness with structure for sublinear time kernel expansions"
    - "deep neural networks with random gaussian weights a universal classification strategy"
    - "flexible multilayer sparse approximations of matrices and applications"
    - "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-4 (induced by source paper "generative moment matching networks"):
  with members:
    - "going deeper with convolutions"
    - "unifying visual semantic embeddings with multimodal neural language models"
    - "neural variational inference and learning in belief networks"
    - "from captions to visual concepts and back"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "show and tell a neural image caption generator"
    - "sequence to sequence learning with neural networks"
    - "overfeat integrated recognition localization and detection using convolutional networks"

Connected through hyperedge HE-5 (induced by source paper "on the error of random fourier features"):
  with members:
    - "scalable kernel methods via doubly stochastic gradients"
    - "b tests low variance kernel two sample tests"

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.AI(Artificial Intelligence)