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: "solving graph compression via optimal transport"
  Abstract: We propose a new approach to graph compression by appeal to optimal transport. The transport problem is seeded with prior information about node importance, attributes, and edges in the graph. The transport formulation can be setup for either directed or undirected graphs, and its dual characterization is cast in terms of distributions over the nodes. The compression pertains to the support of node distributions and makes the problem challenging to solve directly. To this end, we introduce Boolean relaxations and specify conditions under which these relaxations are exact. The relaxations admit algorithms with provably fast convergence. Moreover, we provide an exact $O(d \log d)$ algorithm for the subproblem of projecting a $d$-dimensional vector to transformed simplex constraints. Our method outperforms state-of-the-art compression methods on graph classification.

Connected through hyperedge HE-1 (induced by source paper "copt coordinated optimal transport on graphs"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "graph coarsening with preserved spectral properties"
    - "a unifying framework for spectrum preserving graph sparsification and coarsening"
    - "got an optimal transport framework for graph comparison"
    - "gromov wasserstein alignment of word embedding spaces"
    - "netlsd hearing the shape of a graph"
    - "approximate nearest neighbor search in high dimensions"
    - "graph reduction with spectral and cut guarantees"

Connected through hyperedge HE-2 (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"
    - "gromov wasserstein learning for graph matching and node embedding"
    - "self attention graph pooling"
    - "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-3 (induced by source paper "graph coarsening with preserved spectral properties"):
  with members:
    - "network density of states"
    - "a unifying framework for spectrum preserving graph sparsification and coarsening"
    - "fully dynamic spectral vertex sparsifiers and applications"
    - "netlsd hearing the shape of a graph"
    - "graph reduction with spectral and cut guarantees"
    - "spectrally approximating large graphs with smaller graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "discriminative embeddings of latent variable models for structured data"

Connected through hyperedge HE-4 (induced by source paper "network summarization with preserved spectral properties"):
  with members:
    - "network density of states"
    - "a unifying framework for spectrum preserving graph sparsification and coarsening"
    - "fully dynamic spectral vertex sparsifiers and applications"
    - "netlsd hearing the shape of a graph"
    - "how powerful are graph neural networks"
    - "graph reduction with spectral and cut guarantees"
    - "spectrally approximating large graphs with smaller graphs"
    - "learning graph level representations with gated recurrent 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)