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: "self taught learning of a deep invariant representation for visual tracking via temporal slowness principle"
  Abstract: Visual representation is crucial for visual tracking methodÂ?s performances. Conventionally, visual representations adopted in visual tracking rely on hand-crafted computer vision descriptors. These descriptors were developed generically without considering tracking-specific information. In this paper, we propose to learn complex-valued invariant representations from tracked sequential image patches, via strong temporal slowness constraint and stacked convolutional autoencoders. The deep slow local representations are learned offline on unlabeled data and transferred to the observational model of our proposed tracker. The proposed observational model retains old training samples to alleviate drift, and collect negative samples which are coherent with targetÂ?s motion pattern for better discriminative tracking. With the learned representation and online training samples, a logistic regression classifier is adopted to distinguish target from background, and retrained online to adapt to appearance changes. Subsequently, the observational model is integrated into a particle filter framework to perform visual tracking. Experimental results on various challenging benchmark sequences demonstrate that the proposed tracker performs favorably against several state-of-the-art trackers. HighlightsTemporal slowness principle is exploited for learning tracking representation.Learned invariant representation is decomposed into amplitude and phase features.Higher-level features are learned by stacking autoencoders convolutionally.A novel observational model to counter drift and collect relevant samples online.Tracking experiments show our method is superior to state-of-the-art trackers.

Connected through hyperedge HE-1 (induced by source paper "autoencoder based sample selection for self taught learning"):
  with members:
    - "beyond sharing weights for deep domain adaptation"
    - "multi modal face pose estimation with multi task manifold deep learning"
    - "very deep convolutional networks for large scale image recognition"
    - "self taught object localization with deep networks"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-2 (induced by source paper "learning to track at 100 fps with deep regression networks"):
  with members:
    - "siamese instance search for tracking"
    - "deeptrack learning discriminative feature representations online for robust visual tracking"
    - "learning spatially regularized correlation filters for visual tracking"
    - "transferring rich feature hierarchies for robust visual tracking"
    - "learning multi domain convolutional neural networks for visual tracking"
    - "understanding and diagnosing visual tracking systems"
    - "online tracking by learning discriminative saliency map with convolutional neural network"
    - "robust visual tracking via convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "deep learning of appearance models for online object tracking"):
  with members:
    - "transferring rich feature hierarchies for robust visual tracking"
    - "online tracking by learning discriminative saliency map with convolutional neural network"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

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)