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: "video tracking using learned hierarchical features"
  Abstract: In this paper, we propose an approach to learn hierarchical features for visual object tracking. First, we offline learn features robust to diverse motion patterns from auxiliary video sequences. The hierarchical features are learned via a two-layer convolutional neural network. Embedding the temporal slowness constraint in the stacked architecture makes the learned features robust to complicated motion transformations, which is important for visual object tracking. Then, given a target video sequence, we propose a domain adaptation module to online adapt the pre-learned features according to the specific target object. The adaptation is conducted in both layers of the deep feature learning module so as to include appearance information of the specific target object. As a result, the learned hierarchical features can be robust to both complicated motion transformations and appearance changes of target objects. We integrate our feature learning algorithm into three tracking methods. Experimental results demonstrate that significant improvement can be achieved using our learned hierarchical features, especially on video sequences with complicated motion transformations.

Connected through hyperedge HE-1 (induced by source paper "robust structured group local sparse tracker using deep features"):
  with members:
    - "real time mdnet"
    - "learning to track at 100 fps with deep regression networks"
    - "learning multi domain convolutional neural networks for visual tracking"
    - "staple complementary learners for real time tracking"
    - "very deep convolutional networks for large scale image recognition"
    - "matconvnet convolutional neural networks for matlab"

Connected through hyperedge HE-2 (induced by source paper "correlated logistic model with elastic net regularization for multilabel image classification"):
  with members:
    - "multi view intact space learning"
    - "return of the devil in the details delving deep into convolutional nets"
    - "matconvnet convolutional neural networks for matlab"
    - "maximum margin output coding"
    - "multi label prediction via compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "a comparison study of credit card fraud detection supervised versus unsupervised"):
  with members:
    - "one class adversarial nets for fraud detection"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "a survey of credit card fraud detection techniques data and technique oriented perspective"

Connected through hyperedge HE-4 (induced by source paper "deep learning for image denoising a survey"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "group normalization"
    - "a variational u net for conditional appearance and shape generation"
    - "residual dense network for image super resolution"
    - "correction by projection denoising images with generative adversarial networks"
    - "dilated deep residual network for image denoising"

Connected through hyperedge HE-5 (induced by source paper "distractor aware siamese networks for visual object tracking"):
  with members:
    - "end to end representation learning for correlation filter based tracking"
    - "learning spatial aware regressions for visual tracking"
    - "youtube boundingboxes a large high precision human annotated data set for object detection in video"
    - "uct learning unified convolutional networks for real time visual tracking"
    - "parallel tracking and verifying a framework for real time and high accuracy visual tracking"
    - "crest convolutional residual learning for visual tracking"
    - "learning background aware correlation filters for visual tracking"
    - "end to end flow correlation tracking with spatial temporal attention"

Connected through hyperedge HE-6 (induced by source paper "learning hierarchical features for visual object tracking with recursive neural networks"):
  with members:
    - "online tracking by learning discriminative saliency map with convolutional neural network"

Connected through hyperedge HE-7 (induced by source paper "robust visual tracking via hierarchical convolutional features"):
  with members:
    - "adaptive correlation filters with long term and short term memory for object tracking"
    - "a novel performance evaluation methodology for single target trackers"
    - "eco efficient convolution operators for tracking"
    - "beyond local search tracking objects everywhere with instance specific proposals"
    - "siamese instance search for tracking"
    - "fully convolutional siamese networks for object tracking"
    - "beyond correlation filters learning continuous convolution operators for visual tracking"
    - "learning to track at 100 fps with deep regression networks"

Connected through hyperedge HE-8 (induced by source paper "dual deep network for visual tracking"):
  with members:
    - "deep tracking seeing beyond seeing using recurrent neural networks"
    - "multi bias non linear activation in deep neural networks"
    - "deeptrack learning discriminative feature representations online for robust visual tracking"
    - "inner and inter label propagation salient object detection in the wild"
    - "learning multi domain convolutional neural networks for visual tracking"
    - "unsupervised learning of visual representations using videos"
    - "deep residual learning for image recognition"
    - "fast r cnn"

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)