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: "online object tracking with proposal selection"
  Abstract: Tracking-by-detection approaches are some of the most successful object trackers in recent years. Their success is largely determined by the detector model they learn initially and then update over time. However, under challenging conditions where an object can undergo transformations, e.g., severe rotation, these methods are found to be lacking. In this paper, we address this problem by formulating it as a proposal selection task and making two contributions. The first one is introducing novel proposals estimated from the geometric transformations undergone by the object, and building a rich candidate set for predicting the object location. The second one is devising a novel selection strategy using multiple cues, i.e., detection score and edgeness score computed from state-of-the-art object edges and motion boundaries. We extensively evaluate our approach on the visual object tracking 2014 challenge and online tracking benchmark datasets, and show the best performance.

Connected through hyperedge HE-1 (induced by source paper "visual tracking via dynamic memory networks"):
  with members:
    - "a twofold siamese network for real time object tracking"
    - "learning dynamic memory networks for object tracking"
    - "recurrent filter learning for visual tracking"
    - "end to end representation learning for correlation filter based tracking"
    - "mavot memory augmented video object tracking"
    - "learning spatial aware regressions for visual tracking"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "dual deep network for visual tracking"

Connected through hyperedge HE-2 (induced by source paper "learning to track any object"):
  with members:
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "tracking without bells and whistles"
    - "feelvos fast end to end embedding learning for video object segmentation"
    - "long term tracking in the wild a benchmark"
    - "fast and accurate online video object segmentation via tracking parts"
    - "distractor aware siamese networks for visual object tracking"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "efficient video object segmentation via network modulation"

Connected through hyperedge HE-3 (induced by source paper "a strong feature representation for siamese network tracker"):
  with members:
    - "siamvgg visual tracking using deeper siamese networks"
    - "a twofold siamese network for real time object tracking"
    - "good features to correlate for visual tracking"
    - "unsupervised attention guided image to image translation"
    - "end to end representation learning for correlation filter based tracking"
    - "large margin object tracking with circulant feature maps"
    - "a novel performance evaluation methodology for single target trackers"
    - "sanet structure aware network for visual tracking"

Connected through hyperedge HE-4 (induced by source paper "quadruplet network with one shot learning for fast visual object tracking"):
  with members:
    - "end to end representation learning for correlation filter based tracking"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "large margin object tracking with circulant feature maps"
    - "deep reinforcement learning for visual object tracking in videos"
    - "parallel tracking and verifying a framework for real time and high accuracy visual tracking"
    - "learning background aware correlation filters for visual tracking"
    - "learning feed forward one shot learners"
    - "fully convolutional siamese networks for object tracking"

Connected through hyperedge HE-5 (induced by source paper "siamvgg visual tracking using deeper siamese networks"):
  with members:
    - "a twofold siamese network for real time object tracking"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "end to end representation learning for correlation filter based tracking"
    - "learning spatial aware regressions for visual tracking"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "uct learning unified convolutional networks for real time visual tracking"
    - "a novel performance evaluation methodology for single target trackers"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-6 (induced by source paper "robust visual tracking using multi frame multi feature joint modeling"):
  with members:
    - "parallel tracking and verifying a framework for real time and high accuracy visual tracking"
    - "a novel performance evaluation methodology for single target trackers"
    - "visual tracking via reliable memories"
    - "discriminative scale space tracking"
    - "tracking randomly moving objects on edge box proposals"
    - "an experimental survey on correlation filter based tracking"
    - "learning multi domain convolutional neural networks for visual tracking"
    - "a survey of appearance models in visual object tracking"

Connected through hyperedge HE-7 (induced by source paper "fast visual object tracking with rotated bounding boxes"):
  with members:
    - "a twofold siamese network for real time object tracking"
    - "fast online object tracking and segmentation a unifying approach"
    - "towards a better match in siamese network based visual object tracker"
    - "siamrpn evolution of siamese visual tracking with very deep networks"
    - "human detection and tracking for video surveillance a cognitive science approach"
    - "fully convolutional siamese networks for object tracking"
    - "learning multi domain convolutional neural networks for visual tracking"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-8 (induced by source paper "high speed tracking with multi kernel correlation filters"):
  with members:
    - "end to end representation learning for correlation filter based tracking"
    - "the world of fast moving objects"
    - "need for speed a benchmark for higher frame rate object tracking"
    - "eco efficient convolution operators for tracking"
    - "discriminative scale space tracking"
    - "adaptive decontamination of the training set a unified formulation for discriminative visual tracking"
    - "learning spatially regularized correlation filters for visual tracking"
    - "staple complementary learners for real time tracking"

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)