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: "robust visual tracking via convolutional networks"
  Abstract: Deep networks have been successfully applied to visual tracking by learning a generic representation offline from numerous training images. However the offline training is time-consuming and the learned generic representation may be less discriminative for tracking specific objects. In this paper we present that, even without offline training with a large amount of auxiliary data, simple two-layer convolutional networks can be powerful enough to develop a robust representation for visual tracking. In the first frame, we employ the k-means algorithm to extract a set of normalized patches from the target region as fixed filters, which integrate a series of adaptive contextual filters surrounding the target to define a set of feature maps in the subsequent frames. These maps measure similarities between each filter and the useful local intensity patterns across the target, thereby encoding its local structural information. Furthermore, all the maps form together a global representation, which is built on mid-level features, thereby remaining close to image-level information, and hence the inner geometric layout of the target is also well preserved. A simple soft shrinkage method with an adaptive threshold is employed to de-noise the global representation, resulting in a robust sparse representation. The representation is updated via a simple and effective online strategy, allowing it to robustly adapt to target appearance variations. Our convolution networks have surprisingly lightweight structure, yet perform favorably against several state-of-the-art methods on the CVPR2013 tracking benchmark dataset with 50 challenging videos.

Connected through hyperedge HE-1 (induced by source paper "application level studies of cellular neural network based hardware accelerators"):
  with members:
    - "a proposal for energy efficient cellular neural network based on spintronic devices"
    - "online tracking by learning discriminative saliency map with convolutional neural network"

Connected through hyperedge HE-2 (induced by source paper "deep reinforcement learning for visual object tracking in videos"):
  with members:
    - "perceptual losses for real time style transfer and super resolution"
    - "fully convolutional siamese networks for object tracking"
    - "spatially supervised recurrent convolutional neural networks for visual object tracking"
    - "learning to track at 100 fps with deep regression networks"
    - "deeptrack learning discriminative feature representations online for robust visual tracking"
    - "transferring rich feature hierarchies for robust visual tracking"
    - "unsupervised learning of video representations using lstms"
    - "first step toward model free anonymous object tracking with recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "extraction and classification of diving clips from continuous video footage"):
  with members:
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "action temporal localization in untrimmed videos via multi stage cnns"
    - "deeptrack learning discriminative feature representations online for robust visual tracking"
    - "end to end learning of action detection from frame glimpses in videos"
    - "beyond short snippets deep networks for video classification"
    - "multi scale context aggregation by dilated convolutions"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast r cnn"

Connected through hyperedge HE-4 (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"
    - "self taught learning of a deep invariant representation for visual tracking via temporal slowness principle"
    - "online tracking by learning discriminative saliency map with convolutional neural network"

Connected through hyperedge HE-5 (induced by source paper "deeptrack learning discriminative feature representations online for robust visual tracking"):
  with members:
    - "fast r cnn"
    - "high speed tracking with kernelized correlation filters"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "return of the devil in the details delving deep into convolutional nets"
    - "part based r cnns for fine grained category detection"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-6 (induced by source paper "joint learning of siamese cnns and temporally constrained metrics for tracklet association"):
  with members:
    - "video tracking using learned hierarchical features"
    - "motchallenge 2015 towards a benchmark for multi target tracking"
    - "online tracking by learning discriminative saliency map with convolutional neural network"
    - "matconvnet convolutional neural networks for matlab"
    - "on pairwise costs for network flow multi object tracking"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "bit biologically inspired tracker"):
  with members:
    - "high speed tracking with kernelized correlation filters"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "online object tracking learning and parsing with and or graphs"):
  with members:
    - "a novel performance evaluation methodology for single target trackers"
    - "transferring rich feature hierarchies for robust visual tracking"
    - "learning multi domain convolutional neural networks for visual tracking"
    - "a survey of appearance models in visual object tracking"
    - "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)