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 "probabilistic feature selection and classification vector machine"):
  Abstract: Sparse Bayesian learning is a state-of-the-art supervised learning algorithm that can choose a subset of relevant samples from the input data and make reliable probabilistic predictions. However, in the presence of high-dimensional data with irrelevant features, traditional sparse Bayesian classifiers suffer from performance degradation and low efficiency due to the incapability of eliminating irrelevant features. To tackle this problem, we propose a novel sparse Bayesian embedded feature selection algorithm that adopts truncated Gaussian distributions as both sample and feature priors. The proposed algorithm, called probabilistic feature selection and classification vector machine (PFCVMLP) is able to simultaneously select relevant features and samples for classification tasks. In order to derive the analytical solutions, Laplace approximation is applied to compute approximate posteriors and marginal likelihoods. Finally, parameters and hyperparameters are optimized by the type-II maximum likelihood method. Experiments on three datasets validate the performance of PFCVMLP along two dimensions: classification performance and effectiveness for feature selection. Finally, we analyze the generalization performance and derive a generalization error bound for PFCVMLP. By tightening the bound, the importance of feature selection is demonstrated.

Includes members:
  Member "incremental sparse bayesian ordinal regression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online learning to rank with list level feedback for image filtering")
  Member "identifying stable patterns over time for emotion recognition from eeg":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "jointly aligning and predicting continuous emotion annotations")
  Member "online feature selection with group structure analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robust regression via online feature selection under adversarial data corruption")
  Member "scalable and accurate online feature selection for big data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "causality based feature selection methods and evaluations")
  Member "large scale online feature selection for ultra high dimensional sparse data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "online learning a comprehensive survey")
  Member "variational relevance vector machines":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "line spectral estimation based on compressed sensing with deterministic sub nyquist sampling")
  Member "transductive rademacher complexity and its applications":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "improvability through semi supervised learning a survey of theoretical results")

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)