human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "feature selection a data perspective"):
  Abstract: Feature selection, as a data preprocessing strategy, has been proven to be effective and efficient in preparing data (especially high-dimensional data) for various data-mining and machine-learning problems. The objectives of feature selection include building simpler and more comprehensible models, improving data-mining performance, and preparing clean, understandable data. The recent proliferation of big data has presented some substantial challenges and opportunities to feature selection. In this survey, we provide a comprehensive and structured overview of recent advances in feature selection research. Motivated by current challenges and opportunities in the era of big data, we revisit feature selection research from a data perspective and review representative feature selection algorithms for conventional data, structured data, heterogeneous data and streaming data. Methodologically, to emphasize the differences and similarities of most existing feature selection algorithms for conventional data, we categorize them into four main groups: similarity-based, information-theoretical-based, sparse-learning-based, and statistical-based methods. To facilitate and promote the research in this community, we also present an open source feature selection repository that consists of most of the popular feature selection algorithms (http://featureselection.asu.edu/). Also, we use it as an example to show how to evaluate feature selection algorithms. At the end of the survey, we present a discussion about some open problems and challenges that require more attention in future research.

Includes members:
  Member "gradient boosted feature selection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards federated learning robustness analytics to data heterogeneity")
  Member "challenges of feature selection for big data analytics":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "causality based feature selection methods and evaluations")
  Member "online feature selection with group structure analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "probabilistic feature selection and classification vector machine")
  Member "unsupervised feature selection with adaptive structure learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi participant multi class vertical federated learning")
  Member "attentional neural network feature selection using cognitive feedback":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fsnet feature selection network on high dimensional biological data")
  Member "generalized fisher score for feature selection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")
  Member "towards ultrahigh dimensional feature selection for big data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "simple and deterministic matrix sketching":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "distributed stochastic algorithms for high rate streaming principal component analysis")

Question: Please tell me which class the source paper 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)