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: "unsupervised feature selection with adaptive structure learning"
  Abstract: The problem of feature selection has raised considerable interests in the past decade. Traditional unsupervised methods select the features which can faithfully preserve the intrinsic structures of data, where the intrinsic structures are estimated using all the input features of data. However, the estimated intrinsic structures are unreliable/inaccurate when the redundant and noisy features are not removed. Therefore, we face a dilemma here: one need the true structures of data to identify the informative features, and one need the informative features to accurately estimate the true structures of data. To address this, we propose a unified learning framework which performs structure learning and feature selection simultaneously. The structures are adaptively learned from the results of feature selection, and the informative features are reselected to preserve the refined structures of data. By leveraging the interactions between these two essential tasks, we are able to capture accurate structures and select more informative features. Experimental results on many benchmark data sets demonstrate that the proposed method outperforms many state of the art unsupervised feature selection methods.

Connected through hyperedge HE-1 (induced by source paper "multi participant multi class vertical federated learning"):
  with members:
    - "parallel distributed logistic regression for vertical federated learning without third party coordinator"
    - "measure contribution of participants in federated learning"
    - "advances and open problems in federated learning"
    - "a communication efficient vertical federated learning framework"
    - "a quasi newton method based vertical federated learning framework for logistic regression"
    - "federated machine learning concept and applications"
    - "entity resolution and federated learning get a federated resolution"
    - "feature selection a data perspective"

Connected through hyperedge HE-2 (induced by source paper "a matlab toolbox for feature importance ranking"):
  with members:
    - "an effective feature selection method based on pair wise feature proximity for high dimensional low sample size data"
    - "infinite latent feature selection a probabilistic latent graph based ranking approach"
    - "feature selection library matlab toolbox"
    - "generalized fisher score for feature selection"

Connected through hyperedge HE-3 (induced by source paper "clustering with similarity preserving"):
  with members:
    - "robust graph learning from noisy data"
    - "low rank kernel learning for graph based clustering"
    - "similarity learning via kernel preserving embedding"
    - "twin learning for similarity and clustering a unified kernel approach"
    - "unified spectral clustering with optimal graph"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "structured low rank matrix factorization global optimality algorithms and applications"
    - "kernel spectral clustering and applications"

Connected through hyperedge HE-4 (induced by source paper "robust graph learning from noisy data"):
  with members:
    - "low rank kernel learning for graph based clustering"
    - "similarity learning via kernel preserving embedding"
    - "yotube searching action proposal via recurrent and static regression networks"
    - "multiview spectral clustering via structured low rank matrix factorization"
    - "connections between nuclear norm and frobenius norm based representations"
    - "constructing the l2 graph for robust subspace learning and subspace clustering"
    - "robust principal component analysis on graphs"
    - "robust pca via nonconvex rank approximation"

Connected through hyperedge HE-5 (induced by source paper "unsupervised feature selection based on adaptive similarity learning and subspace clustering"):
  with members:
    - "feature selection a data perspective"
    - "relevant based structure learning for feature selection"
    - "robust subspace clustering"
    - "optimization with sparsity inducing penalties"

Connected through hyperedge HE-6 (induced by source paper "zero shot feature selection via exploiting semantic knowledge"):
  with members:
    - "less is more zero shot learning from online textual documents with noise suppression"
    - "multi cue zero shot learning with strong supervision"
    - "zero shot recognition with unreliable attributes"

Connected through hyperedge HE-7 (induced by source paper "similarity learning via kernel preserving embedding"):
  with members:
    - "low rank kernel learning for graph based clustering"
    - "twin learning for similarity and clustering a unified kernel approach"
    - "unified spectral clustering with optimal graph"

Connected through hyperedge HE-8 (induced by source paper "dsl discriminative subgraph learning via sparse self representation"):
  with members:
    - "discriminative subnetworks with regularized spectral learning for global state network data"
    - "sparse subspace clustering algorithm theory and applications"

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)