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: "search convolutional neural networks"
  Abstract: We present a new deterministic relational model derived from convolutional neural networks. Search-Convolutional Neural Networks (SCNNs) extend the notion of convolution to graph search to construct a rich latent representation that extracts local behavior from general graph-structured data. Unlike other neural network models that take graph-structured data as input, SCNNs have a parameterization that is independent of input size, a property that enables transfer learning between datasets. SCNNs can be applied to a wide variety of prediction tasks, including node classification, community detection, and link prediction. Our results indicate that SCNNs can offer considerable lift over off-the-shelf classifiers and simple multilayer perceptrons, and comparable performance to state-of-the-art probabilistic graphical models at considerably lower computational cost.

Connected through hyperedge HE-1 (induced by source paper "get rid of suspended animation problem deep diffusive neural network on graph semi supervised classification"):
  with members:
    - "adagcn adaboosting graph convolutional networks into deep models"
    - "gresnet graph residual network for reviving deep gnns from suspended animation"
    - "graphnas graph neural architecture search with reinforcement learning"
    - "graph neural networks for small graph and giant network representation learning an overview"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "combinatorial optimization with graph convolutional networks and guided tree search"
    - "deep loopy neural network model for graph structured data representation learning"

Connected through hyperedge HE-2 (induced by source paper "adaptive hierarchical down sampling for point cloud classification"):
  with members:
    - "so net self organizing network for point cloud analysis"
    - "dynamic graph cnn for learning on point clouds"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"
    - "mining point cloud local structures by kernel correlation and graph pooling"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"
    - "learning shape correspondence with anisotropic convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "semi supervised learning on graphs based on local label distributions"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "struc2vec learning node representations from structural identity"
    - "lasagne locality and structure aware graph node embedding"
    - "representation learning on graphs methods and applications"
    - "graph convolutional networks for classification with a structured label space"
    - "neural message passing for quantum chemistry"
    - "gated graph sequence neural networks"
    - "node2vec scalable feature learning for networks"

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)