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: "online feature selection with group structure analysis"
  Abstract: Online selection of dynamic features has attracted intensive interest in recent years. However, existing online feature selection methods evaluate features individually and ignore the underlying structure of a feature stream. For instance, in image analysis, features are generated in groups which represent color, texture, and other visual information. Simply breaking the group structure in feature selection may degrade performance. Motivated by this observation, we formulate the problem as an online group feature selection. The problem assumes that features are generated individually but there are group structures in the feature stream. To the best of our knowledge, this is the first time that the correlation among streaming features has been considered in the online feature selection process. To solve this problem, we develop a novel online group feature selection method named OGFS. Our proposed approach consists of two stages: online intra-group selection and online inter-group selection. In the intra-group selection, we design a criterion based on spectral analysis to select discriminative features in each group. In the inter-group selection, we utilize a linear regression model to select an optimal subset. This two-stage procedure continues until there are no more features arriving or some predefined stopping conditions are met. Finally, we apply our method to multiple tasks including image classification and face verification. Extensive empirical studies performed on real-world and benchmark data sets demonstrate that our method outperforms other state-of-the-art online feature selection methods.

Connected through hyperedge HE-1 (induced by source paper "probabilistic feature selection and classification vector machine"):
  with members:
    - "incremental sparse bayesian ordinal regression"
    - "identifying stable patterns over time for emotion recognition from eeg"
    - "scalable and accurate online feature selection for big data"
    - "large scale online feature selection for ultra high dimensional sparse data"
    - "variational relevance vector machines"
    - "transductive rademacher complexity and its applications"

Connected through hyperedge HE-2 (induced by source paper "robust regression via online feature selection under adversarial data corruption"):
  with members:
    - "distributed self paced learning in alternating direction method of multipliers"
    - "water disaggregation via shape features based bayesian discriminative sparse coding"
    - "online and distributed robust regressions under adversarial data corruption"
    - "robust regression via hard thresholding"
    - "scalable and accurate online feature selection for big data"

Connected through hyperedge HE-3 (induced by source paper "moanofs multi objective automated negotiation based online feature selection system for big data classification"):
  with members:
    - "large scale online feature selection for ultra high dimensional sparse data"
    - "massive scale online feature selection for sparse ultra high dimensional data"
    - "sparse online learning via truncated gradient"

Connected through hyperedge HE-4 (induced by source paper "feature selection a data perspective"):
  with members:
    - "gradient boosted feature selection"
    - "challenges of feature selection for big data analytics"
    - "unsupervised feature selection with adaptive structure learning"
    - "attentional neural network feature selection using cognitive feedback"
    - "generalized fisher score for feature selection"
    - "towards ultrahigh dimensional feature selection for big data"
    - "simple and deterministic matrix sketching"
    - "supervised random walks predicting and recommending links in social 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.CV(Computer Vision and Pattern Recognition)