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: "graphnas graph neural architecture search with reinforcement learning"
  Abstract: Graph Neural Networks (GNNs) have been popularly used for analyzing non-Euclidean data such as social network data and biological data. Despite their success, the design of graph neural networks requires a lot of manual work and domain knowledge. In this paper, we propose a Graph Neural Architecture Search method (GraphNAS for short) that enables automatic search of the best graph neural architecture based on reinforcement learning. Specifically, GraphNAS first uses a recurrent network to generate variable-length strings that describe the architectures of graph neural networks, and then trains the recurrent network with reinforcement learning to maximize the expected accuracy of the generated architectures on a validation data set. Extensive experimental results on node classification tasks in both transductive and inductive learning settings demonstrate that GraphNAS can achieve consistently better performance on the Cora, Citeseer, Pubmed citation network, and protein-protein interaction network. On node classification tasks, GraphNAS can design a novel network architecture that rivals the best human-invented architecture in terms of test set accuracy.

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"
    - "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"
    - "geometric deep learning on graphs and manifolds using mixture model cnns"

Connected through hyperedge HE-2 (induced by source paper "auto gnn neural architecture search of graph neural networks"):
  with members:
    - "agan towards automated design of generative adversarial networks"
    - "exploring randomly wired neural networks for image recognition"
    - "single path one shot neural architecture search with uniform sampling"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "fast graph representation learning with pytorch geometric"
    - "reinforced evolutionary neural architecture search"
    - "regularized evolution for image classifier architecture search"
    - "attention models in graphs a survey"

Connected through hyperedge HE-3 (induced by source paper "gresnet graph residual network for reviving deep gnns from suspended animation"):
  with members:
    - "heterogeneous graph neural networks for malicious account detection"
    - "graph optimized convolutional networks"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "graph neural networks a review of methods and applications"
    - "norm preservation why residual networks can become extremely deep"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "invertible residual networks"
    - "representing smooth functions as compositions of near identity functions with implications for deep network optimization"

Connected through hyperedge HE-4 (induced by source paper "dimensional reweighting graph convolutional networks"):
  with members:
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "dual primal graph convolutional networks"
    - "geniepath graph neural networks with adaptive receptive paths"
    - "gaan gated attention networks for learning on large and spatiotemporal graphs"
    - "large scale learnable graph convolutional networks"
    - "adaptive sampling towards fast graph representation learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"

Connected through hyperedge HE-5 (induced by source paper "pre train and learn preserve global information for graph neural networks"):
  with members:
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "representation learning on graphs with jumping knowledge networks"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "n gcn multi scale graph convolution for semi supervised node classification"
    - "learning to make predictions on graphs with autoencoders"
    - "focal loss for dense object detection"
    - "attention is all you need"

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)