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 loopy neural network model for graph structured data representation learning"
  Abstract: Existing deep learning models may encounter great challenges in handling graph structured data. In this paper, we introduce a new deep learning model for graph data specifically, namely the deep loopy neural network. Significantly different from the previous deep models, inside the deep loopy neural network, there exist a large number of loops created by the extensive connections among nodes in the input graph data, which makes model learning an infeasible task. To resolve such a problem, in this paper, we will introduce a new learning algorithm for the deep loopy neural network specifically. Instead of learning the model variables based on the original model, in the proposed learning algorithm, errors will be back-propagated through the edges in a group of extracted spanning trees. Extensive numerical experiments have been done on several real-world graph datasets, and the experimental results demonstrate the effectiveness of both the proposed model and the learning algorithm in handling graph data.

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

Connected through hyperedge HE-2 (induced by source paper "graph bert only attention is needed for learning graph representations"):
  with members:
    - "isonn isomorphic neural network for graph representation learning and classification"
    - "transformer xl attentive language models beyond a fixed length context"
    - "gresnet graph residual network for reviving deep gnns from suspended animation"
    - "segen sample ensemble genetic evolutionary network model"
    - "rethinking imagenet pre training"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "junction tree variational autoencoder for molecular graph generation"
    - "attention is all you need"

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"
    - "graphnas graph neural architecture search with reinforcement learning"
    - "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"

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)