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: "learning generative models across incomparable spaces"
  Abstract: Generative Adversarial Networks have shown remarkable success in learning a distribution that faithfully recovers a reference distribution in its entirety. However, in some cases, we may want to only learn some aspects (e.g., cluster or manifold structure), while modifying others (e.g., style, orientation or dimension). In this work, we propose an approach to learn generative models across such incomparable spaces, and demonstrate how to steer the learned distribution towards target properties. A key component of our model is the Gromov-Wasserstein distance, a notion of discrepancy that compares distributions relationally rather than absolutely. While this framework subsumes current generative models in identically reproducing distributions, its inherent flexibility allows application to tasks in manifold learning, relational learning and cross-domain learning.

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"
    - "gromov wasserstein factorization models for graph clustering"
    - "gromov wasserstein alignment of word embedding spaces"
    - "sliced wasserstein autoencoder an embarrassingly simple generative model"
    - "the gromov wasserstein distance between networks and stable network invariants"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "multi view generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "gromov wasserstein learning for graph matching and node embedding"):
  with members:
    - "gromov wasserstein alignment of word embedding spaces"
    - "graph convolutional neural networks for web scale recommender systems"
    - "the gromov wasserstein distance between networks and stable network invariants"
    - "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-3 (induced by source paper "unsupervised hierarchy matching with optimal transport over hyperbolic spaces"):
  with members:
    - "riemannian adaptive optimization methods"
    - "gromov wasserstein alignment of word embedding spaces"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "hyperbolic neural networks"
    - "improving gans using optimal transport"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "representation tradeoffs for hyperbolic embeddings"

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"
    - "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-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"
    - "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"

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)