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: "deep weisfeiler lehman assignment kernels via multiple kernel learning"
  Abstract: Kernels for structured data are commonly obtained by decomposing objects into their parts and adding up the similarities between all pairs of parts measured by a base kernel. Assignment kernels are based on an optimal bijection between the parts and have proven to be an effective alternative to the established convolution kernels. We explore how the base kernel can be learned as part of the classification problem. We build on the theory of valid assignment kernels derived from hierarchies defined on the parts. We show that the weights of this hierarchy can be optimized via multiple kernel learning. We apply this result to learn vertex similarities for the Weisfeiler-Lehman optimal assignment kernel for graph classification. We present first experimental results which demonstrate the feasibility and effectiveness of the approach.

Connected through hyperedge HE-1 (induced by source paper "embeddings and representation learning for structured data"):
  with members:
    - "tree edit distance learning via adaptive symbol embeddings"
    - "constrained graph variational autoencoders for molecule design"
    - "hierarchical graph representation learning with differentiable pooling"
    - "contextual graph markov model a deep and generative approach to graph processing"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "tree to tree neural networks for program translation"
    - "junction tree variational autoencoder for molecular graph generation"
    - "lstm a search space odyssey"

Connected through hyperedge HE-2 (induced by source paper "a survey on graph kernels"):
  with members:
    - "a unifying view of explicit and implicit feature maps of graph kernels"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "splinecnn fast geometric deep learning with continuous b spline kernels"
    - "neural message passing for quantum chemistry"
    - "semi supervised classification with graph convolutional networks"
    - "on valid optimal assignment kernels and applications to graph classification"
    - "faster kernels for graphs with continuous attributes via hashing"
    - "convolutional networks on graphs for learning molecular fingerprints"

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)