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: "gromov wasserstein learning for graph matching and node embedding"
  Abstract: A novel Gromov-Wasserstein learning framework is proposed to jointly match (align) graphs and learn embedding vectors for the associated graph nodes. Using Gromov-Wasserstein discrepancy, we measure the dissimilarity between two graphs and find their correspondence, according to the learned optimal transport. The node embeddings associated with the two graphs are learned under the guidance of the optimal transport, the distance of which not only reflects the topological structure of each graph but also yields the correspondence across the graphs. These two learning steps are mutually-beneficial, and are unified here by minimizing the Gromov-Wasserstein discrepancy with structural regularizers. This framework leads to an optimization problem that is solved by a proximal point method. We apply the proposed method to matching problems in real-world networks, and demonstrate its superior performance compared to alternative approaches.

Connected through hyperedge HE-1 (induced by source paper "deep graph matching consensus"):
  with members:
    - "simgnn a neural network approach to fast graph similarity computation"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "fast detection of maximum common subgraph via deep q learning"):
  with members:
    - "simgnn a neural network approach to fast graph similarity computation"
    - "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"
    - "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 "unsupervised learning of graph hierarchical abstractions with differentiable coarsening and optimal transport"):
  with members:
    - "lanczosnet multi scale deep graph convolutional networks"
    - "graph u nets"
    - "self attention graph pooling"
    - "solving graph compression via optimal transport"
    - "provably powerful graph networks"
    - "online planner selection with graph neural networks and adaptive scheduling"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"

Connected through hyperedge HE-4 (induced by source paper "wasserstein weisfeiler lehman graph kernels"):
  with members:
    - "on the definiteness of earth mover s distance and its relation to set intersection"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "semi supervised classification with graph convolutional networks"
    - "on valid optimal assignment kernels and applications to graph classification"
    - "faster kernels for graphs with continuous attributes via hashing"
    - "convolutional networks on graphs for learning molecular fingerprints"
    - "sliced wasserstein kernels for probability distributions"
    - "learning with a wasserstein loss"

Connected through hyperedge HE-5 (induced by source paper "gromov wasserstein factorization models for graph clustering"):
  with members:
    - "scalable gromov wasserstein learning for graph partitioning and matching"
    - "learning generative models across incomparable spaces"
    - "gromov wasserstein alignment of word embedding spaces"
    - "hierarchical graph representation learning with differentiable pooling"
    - "the gromov wasserstein distance between networks and stable network invariants"
    - "semi supervised classification with graph convolutional networks"
    - "deep convolutional networks on graph structured data"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "scalable gromov wasserstein learning for graph partitioning and matching"):
  with members:
    - "learning generative models across incomparable spaces"
    - "gromov wasserstein alignment of word embedding spaces"
    - "the gromov wasserstein distance between networks and stable network invariants"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "node2vec scalable feature learning for networks"
    - "point set registration coherent point drift"

Connected through hyperedge HE-7 (induced by source paper "fused gromov wasserstein alignment for hawkes processes"):
  with members:
    - "the neural hawkes process a neurally self modulating multivariate point process"

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)