human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "the gaussiansketch for almost relative error kernel distance"):
  Abstract: We introduce a two versions of a new sketch for approximately embedding the Gaussian kernel into Euclidean inner product space. These work by truncating infinite expansions of the Gaussian inner product, and carefully invoking the TensorSketch. After providing concentration and approximation properties of these sketches, we demonstrate them to approximate the kernel distance between points sets. These sketches yield almost $(1+\varepsilon)$-relative error, but with a small additive $\alpha$ term. In the first variants the dependence on $1/\alpha$ is logarithmic, but has a separate exponential dependence on the original dimension $d$. In the second variant, the dependence on $1/\alpha$ is still sub-polynomial, but the dependence on $d$ is linear.

Includes members:
  Member "hashing based estimators for kernel density in high dimensions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "race sub linear memory sketches for approximate near neighbor search on streaming data")
  Member "random fourier features for kernel ridge regression approximation bounds and statistical guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "coke communication censored kernel learning for decentralized non parametric learning")
  Member "locality sensitive hashing of curves":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dynamic time warping in strongly subquadratic time algorithms for the low distance regime and approximate evaluation")
  Member "sublinear time low rank approximation of positive semidefinite matrices":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarially robust low dimensional representations")
  Member "is input sparsity time possible for kernel low rank approximation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fast on the fly retraining free sparsification of convolutional neural networks")
  Member "optimality of the johnson lindenstrauss lemma":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "solving tall dense linear programs in nearly linear time")
  Member "streaming kernel principal component analysis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "streaming kernel pca with tilde o sqrt n random features")
  Member "fastmmd ensemble of circular discrepancy for efficient two sample test":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning implicit generative models with the method of learned moments")

Question: Please tell me which class the source paper 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)