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: "motifnet a motif based graph convolutional network for directed graphs"
  Abstract: Deep learning on graphs and in particular, graph convolutional neural networks, have recently attracted significant attention in the machine learning community. Many of such techniques explore the analogy between the graph Laplacian eigenvectors and the classical Fourier basis, allowing to formulate the convolution as a multiplication in the spectral domain. One of the key drawback of spectral CNNs is their explicit assumption of an undirected graph, leading to a symmetric Laplacian matrix with orthogonal eigendecomposition. In this work we propose MotifNet, a graph CNN capable of dealing with directed graphs by exploiting local graph motifs. We present experimental evidence showing the advantage of our approach on real data.

Connected through hyperedge HE-1 (induced by source paper "can graph neural networks count substructures"):
  with members:
    - "composing molecules with multiple property constraints"
    - "generalization and representational limits of graph neural networks"
    - "universal invariant and equivariant graph neural networks"
    - "g2sat learning to generate sat formulas"
    - "cognitive graph for multi hop reading comprehension at scale"
    - "on the universality of invariant networks"
    - "what graph neural networks cannot learn depth vs width"
    - "gnn explainer a tool for post hoc explanation of graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "meshgan non linear 3d morphable models of faces"):
  with members:
    - "generating 3d faces using convolutional mesh autoencoders"
    - "inverting the generator of a generative adversarial network ii"
    - "relational inductive biases deep learning and graph networks"
    - "escaping from collapsing modes in a constrained space"
    - "a simple approach to intrinsic correspondence learning on unstructured 3d meshes"
    - "nonlinear 3d face morphable model"
    - "variational autoencoders for deforming 3d mesh models"
    - "began boundary equilibrium generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "learning representations of graph data a survey"):
  with members:
    - "fast sequence based embedding with diffusion graphs"
    - "adaptive graph convolutional neural networks"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "learning deep generative models of graphs"
    - "relational inductive biases deep learning and graph networks"
    - "deriving neural architectures from sequence and graph kernels"
    - "inductive representation learning on large graphs"
    - "sparse diffusion convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "fake news detection on social media using geometric deep learning"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "studying fake news via network analysis detection and mitigation"
    - "on the convergence of adam and beyond"
    - "dual primal graph convolutional networks"
    - "fake news a survey of research detection methods and opportunities"
    - "dynamic graph cnn for learning on point clouds"
    - "peernets exploiting peer wisdom against adversarial attacks"
    - "self normalizing neural networks"

Connected through hyperedge HE-5 (induced by source paper "on the choice of graph neural network architectures"):
  with members:
    - "graph neural networks exponentially lose expressive power for node classification"
    - "simplifying graph convolutional networks"
    - "fast graph representation learning with pytorch geometric"
    - "structured agents for physical construction"
    - "a comprehensive survey on graph neural networks"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "dual primal graph convolutional networks"
    - "pitfalls of graph neural network evaluation"

Connected through hyperedge HE-6 (induced by source paper "learning individual causal effects from networked observational data"):
  with members:
    - "learning counterfactual representations for estimating individual dose response curves"
    - "using network motifs to characterize temporal network evolution leading to diffusion inhibition"
    - "graph convolutional neural networks for web scale recommender systems"
    - "linked causal variational autoencoder for inferring paired spillover effects"
    - "perfect match a simple method for learning representations for counterfactual inference with neural networks"
    - "a survey of learning causality with data problems and methods"
    - "confounding robust policy improvement"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "dynamic graph cnn for learning on point clouds"):
  with members:
    - "generating 3d faces using convolutional mesh autoencoders"
    - "self supervised learning of dense shape correspondence"
    - "feature denoising for improving adversarial robustness"
    - "point convolutional neural networks by extension operators"
    - "splatnet sparse lattice networks for point cloud processing"
    - "point cloud gan"
    - "pcpnet learning local shape properties from raw point clouds"
    - "deep functional maps structured prediction for dense shape correspondence"

Connected through hyperedge HE-8 (induced by source paper "peernets exploiting peer wisdom against adversarial attacks"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "deep dictionary learning a parametric network approach"
    - "adversarial attacks and defences competition"
    - "dynamic graph cnn for learning on point clouds"
    - "non local neural networks"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "video frame interpolation via adaptive convolution"

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)