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 "learning multiple levels of representations with kernel machines"):
  Abstract: We propose a connectionist-inspired kernel machine model with three key advantages over traditional kernel machines. First, it is capable of learning distributed and hierarchical representations. Second, its performance is highly robust to the choice of kernel function. Third, the solution space is not limited to the span of images of training data in reproducing kernel Hilbert space (RKHS). Together with the architecture, we propose a greedy learning algorithm that allows the proposed multilayer network to be trained layer-wise without backpropagation by optimizing the geometric properties of images in RKHS. With a single fixed generic kernel for each layer and two layers in total, our model compares favorably with state-of-the-art multiple kernel learning algorithms using significantly more kernels and popular deep architectures on widely used classification benchmarks.

Includes members:
  Member "deep forest":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "techniques all classifiers can learn from deep networks models optimizations and regularization")
  Member "layer wise training of deep networks using kernel similarity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "heterogeneous multilayer generalized operational perceptron")
  Member "deep forest towards an alternative to deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fast dense residual network enhancing global dense feature flow for text recognition")
  Member "fashion mnist a novel image dataset for benchmarking machine learning algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "decoupled neural interfaces using synthetic gradients":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sideways depth parallel training of video models")
  Member "learning functions when is deep better than shallow":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning the mapping mathbf x mapsto sum_ i 1 d x_i 2 the cost of finding the needle in a haystack")
  Member "a neural algorithm of artistic style":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deformation aware unpaired image translation for pose estimation on laboratory animals")
  Member "deep kernel learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "effective diversity in population based reinforcement 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)