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 "local group invariant representations via orbit embeddings"):
  Abstract: Invariance to nuisance transformations is one of the desirable properties of effective representations. We consider transformations that form a \emph{group} and propose an approach based on kernel methods to derive local group invariant representations. Locality is achieved by defining a suitable probability distribution over the group which in turn induces distributions in the input feature space. We learn a decision function over these distributions by appealing to the powerful framework of kernel methods and generate local invariant random feature maps via kernel approximations. We show uniform convergence bounds for kernel approximation and provide excess risk bounds for learning with these features. We evaluate our method on three real datasets, including Rotated MNIST and CIFAR-10, and observe that it outperforms competing kernel based approaches. The proposed method also outperforms deep CNN on Rotated-MNIST and performs comparably to the recently proposed group-equivariant CNN.

Includes members:
  Member "recycling randomness with structure for sublinear time kernel expansions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on dimension free tail inequalities for sums of random matrices and applications")
  Member "learning from conditional distributions via dual kernel embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gradientdice rethinking generalized offline estimation of stationary values")
  Member "structured adaptive and random spinners for fast machine learning computations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient data analytics on augmented similarity triplets")
  Member "group equivariant convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a permutation equivariant neural network architecture for auction design")
  Member "learning with group invariant features a kernel perspective":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "parametrizing filters of a cnn with a gan")
  Member "on invariance and selectivity in representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "are we done with object recognition the icub robot s perspective")
  Member "scalable kernel methods via doubly stochastic gradients":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "neural kernels without tangents")
  Member "how to scale up kernel methods to be as good as deep neural nets":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "large scale kernel methods and applications to lifelong robot learning")

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)