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: "simgnn a neural network approach to fast graph similarity computation"
  Abstract: Graph similarity search is among the most important graph-based applications, e.g. finding the chemical compounds that are most similar to a query compound. Graph similarity computation, such as Graph Edit Distance (GED) and Maximum Common Subgraph (MCS), is the core operation of graph similarity search and many other applications, but very costly to compute in practice. Inspired by the recent success of neural network approaches to several graph applications, such as node or graph classification, we propose a novel neural network based approach to address this classic yet challenging graph problem, aiming to alleviate the computational burden while preserving a good performance.  The proposed approach, called SimGNN, combines two strategies. First, we design a learnable embedding function that maps every graph into a vector, which provides a global summary of a graph. A novel attention mechanism is proposed to emphasize the important nodes with respect to a specific similarity metric. Second, we design a pairwise node comparison method to supplement the graph-level embeddings with fine-grained node-level information. Our model achieves better generalization on unseen graphs, and in the worst case runs in quadratic time with respect to the number of nodes in two graphs. Taking GED computation as an example, experimental results on three real graph datasets demonstrate the effectiveness and efficiency of our approach. Specifically, our model achieves smaller error rate and great time reduction compared against a series of baselines, including several approximation algorithms on GED computation, and many existing graph neural network based models. To the best of our knowledge, we are among the first to adopt neural networks to explicitly model the similarity between two graphs, and provide a new direction for future research on graph similarity computation and graph similarity search.

Connected through hyperedge HE-1 (induced by source paper "deep graph matching consensus"):
  with members:
    - "learning combinatorial embedding networks for deep graph matching"
    - "cross lingual knowledge graph alignment via graph matching neural network"
    - "computing optimal assignments in linear time for approximate graph matching"
    - "scalable gromov wasserstein learning for graph partitioning and matching"
    - "deep adversarial network alignment"
    - "graph neural networks for user identity linkage"
    - "kergm kernelized graph matching"
    - "gromov wasserstein learning for graph matching and node embedding"

Connected through hyperedge HE-2 (induced by source paper "fast detection of maximum common subgraph via deep q learning"):
  with members:
    - "learning combinatorial embedding networks for deep graph matching"
    - "learning to route in similarity graphs"
    - "a unifying framework for spectrum preserving graph sparsification and coarsening"
    - "scalable gromov wasserstein learning for graph partitioning and matching"
    - "gromov wasserstein learning for graph matching and node embedding"
    - "a learning based branch and bound for maximum common subgraph problems"
    - "fast graph representation learning with pytorch geometric"
    - "graph matching networks for learning the similarity of graph structured objects"

Connected through hyperedge HE-3 (induced by source paper "graph neural distance metric learning with graph bert"):
  with members:
    - "graph bert only attention is needed for learning graph representations"
    - "adagcn adaboosting graph convolutional networks into deep models"
    - "isonn isomorphic neural network for graph representation learning and classification"
    - "structure fusion based on graph convolutional networks for semi supervised classification"
    - "gresnet graph residual network for reviving deep gnns from suspended animation"
    - "wasserstein weisfeiler lehman graph kernels"
    - "graph neural networks for small graph and giant network representation learning an overview"
    - "graph matching networks for learning the similarity of graph structured objects"

Connected through hyperedge HE-4 (induced by source paper "learn to generate time series conditioned graphs with generative adversarial nets"):
  with members:
    - "labeled graph generative adversarial networks"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning deep generative models of graphs"
    - "deep graph translation"
    - "junction tree variational autoencoder for molecular graph generation"
    - "simple recurrent units for highly parallelizable recurrence"
    - "instance normalization the missing ingredient for fast stylization"

Connected through hyperedge HE-5 (induced by source paper "deep graph similarity learning a survey"):
  with members:
    - "deep graph matching consensus"
    - "dynamic graph convolutional networks"
    - "glmnet graph learning matching networks for feature matching"
    - "from community to role based graph embeddings"
    - "learning combinatorial embedding networks for deep graph matching"
    - "community preserving graph convolutions for structural and functional joint embedding of brain networks"
    - "generalizing from a few examples a survey on few shot learning"
    - "rethinking kernel methods for node representation learning on graphs"

Connected through hyperedge HE-6 (induced by source paper "unsupervised inductive graph level representation learning via graph graph proximity"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "link prediction based on graph neural networks"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "drug similarity integration through attentive multi view graph auto encoders"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-7 (induced by source paper "graph learning network a structure learning algorithm"):
  with members:
    - "decoding molecular graph embeddings with reinforcement learning"
    - "link prediction based on graph neural networks"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning structural node embeddings via diffusion wavelets"
    - "learning deep generative models of graphs"
    - "encoding sentences with graph convolutional networks for semantic role labeling"
    - "gated graph sequence neural 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)