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 "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.

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 "two layer feature reduction for sparse group lasso via decomposition of convex sets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "layered group sparse beamforming for cache enabled green wireless networks")
  Member "unified structured learning for simultaneous human pose estimation and garment attribute classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fashion apparel detection the role of deep convolutional neural network and pose dependent priors")
  Member "supervised feature selection via dependence estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "similarity of neural network representations revisited")

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.CV(Computer Vision and Pattern Recognition)