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: "adaptive compressive tracking via online vector boosting feature selection"
  Abstract: Recently, the compressive tracking (CT) method has attracted much attention due to its high efficiency, but it cannot well deal with the large scale target appearance variations due to its data-independent random projection matrix that results in less discriminative features. To address this issue, in this paper we propose an adaptive CT approach, which selects the most discriminative features to design an effective appearance model. Our method significantly improves CT in three aspects: Firstly, the most discriminative features are selected via an online vector boosting method. Secondly, the object representation is updated in an effective online manner, which preserves the stable features while filtering out the noisy ones. Finally, a simple and effective trajectory rectification approach is adopted that can make the estimated location more accurate. Extensive experiments on the CVPR2013 tracking benchmark demonstrate the superior performance of our algorithm compared over state-of-the-art tracking algorithms.

Connected through hyperedge HE-1 (induced by source paper "sg fcn a motion and memory based deep learning model for video saliency detection"):
  with members:
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "exploring models and data for remote sensing image caption generation"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "exploiting inter image similarity and ensemble of extreme learners for fixation prediction using deep features"
    - "geometric hypergraph learning for visual tracking"
    - "deepsaliency multi task deep neural network model for salient object detection"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

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)