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: "hierarchical optimal transport for document representation"
  Abstract: The ability to measure similarity between documents enables intelligent summarization and analysis of large corpora. Past distances between documents suffer from either an inability to incorporate semantic similarities between words or from scalability issues. As an alternative, we introduce hierarchical optimal transport as a meta-distance between documents, where documents are modeled as distributions over topics, which themselves are modeled as distributions over words. We then solve an optimal transport problem on the smaller topic space to compute a similarity score. We give conditions on the topics under which this construction defines a distance, and we relate it to the word mover's distance. We evaluate our technique for $k$-NN classification and show better interpretability and scalability with comparable performance to current methods at a fraction of the cost.

Connected through hyperedge HE-1 (induced by source paper "learning autoencoders with relational regularization"):
  with members:
    - "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"
    - "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 "geometric dataset distances via optimal transport"):
  with members:
    - "adaptive gradient based meta learning methods"
    - "dynamics and reachability of learning tasks"
    - "transferability and hardness of supervised classification tasks"
    - "task2vec task embedding for meta learning"
    - "learning embeddings into entropic wasserstein spaces"
    - "gromov wasserstein alignment of word embedding spaces"
    - "deep learning for classical japanese literature"
    - "deep contextualized word representations"

Connected through hyperedge HE-3 (induced by source paper "statistical optimal transport posed as learning kernel embedding"):
  with members:
    - "kernel conditional density operators"
    - "kernel wasserstein distance"
    - "gromov wasserstein alignment of word embedding spaces"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "tsallis regularized optimal transport and ecological inference"
    - "learning with a wasserstein loss"
    - "on integral probability metrics φ divergences and binary classification"

Connected through hyperedge HE-4 (induced by source paper "scalable nearest neighbor search for optimal transport"):
  with members:
    - "scalable fair clustering"
    - "approximate nearest neighbor search in high dimensions"
    - "practical data dependent metric compression with provable guarantees"
    - "sketching and embedding are equivalent for norms"
    - "distributed representations of words and phrases and their compositionality"
    - "ramsey partitions and proximity data structures"
    - "planar earthmover is not in l_1"

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)