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: "the gromov wasserstein distance between networks and stable network invariants"
  Abstract: We define a metric---the Network Gromov-Wasserstein distance---on weighted, directed networks that is sensitive to the presence of outliers. In addition to proving its theoretical properties, we supply easily computable network invariants that approximate this distance by means of lower bounds.

Connected through hyperedge HE-1 (induced by source paper "learning autoencoders with relational regularization"):
  with members:
    - "hierarchical optimal transport for document representation"
    - "scalable gromov wasserstein learning for graph partitioning and matching"
    - "learning generative models across incomparable spaces"
    - "gromov wasserstein factorization models for graph clustering"
    - "gromov wasserstein alignment of word embedding spaces"
    - "sliced wasserstein autoencoder an embarrassingly simple generative model"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "multi view generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "learning generative models across incomparable spaces"):
  with members:
    - "gromov wasserstein alignment of word embedding spaces"
    - "improving gans using optimal transport"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "mcgan mean and covariance feature matching gan"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "emnist an extension of mnist to handwritten letters"
    - "mmd gan towards deeper understanding of moment matching network"

Connected through hyperedge HE-3 (induced by source paper "gromov wasserstein learning for graph matching and node embedding"):
  with members:
    - "learning generative models across incomparable spaces"
    - "gromov wasserstein alignment of word embedding spaces"
    - "graph convolutional neural networks for web scale recommender systems"
    - "adversarial text generation via feature mover s distance"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "unsupervised alignment of embeddings with wasserstein procrustes"
    - "anonymous walk embeddings"
    - "triangular alignment tame a tensor based approach for higher order network alignment"

Connected through hyperedge HE-4 (induced by source paper "gromov wasserstein factorization models for graph clustering"):
  with members:
    - "scalable gromov wasserstein learning for graph partitioning and matching"
    - "gromov wasserstein learning for graph matching and node embedding"
    - "learning generative models across incomparable spaces"
    - "gromov wasserstein alignment of word embedding spaces"
    - "hierarchical graph representation learning with differentiable pooling"
    - "semi supervised classification with graph convolutional networks"
    - "deep convolutional networks on graph structured data"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "scalable gromov wasserstein learning for graph partitioning and matching"):
  with members:
    - "gromov wasserstein learning for graph matching and node embedding"
    - "learning generative models across incomparable spaces"
    - "gromov wasserstein alignment of word embedding spaces"
    - "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-6 (induced by source paper "mapper comparison with wasserstein metrics"):
  with members:
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "deltacon a principled massive graph similarity function"
    - "netsimile a scalable approach to size independent network similarity"

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.DM(Discrete Mathematics)