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: "deeptrack learning discriminative feature representations online for robust visual tracking"
  Abstract: Deep neural networks, albeit their great success on feature learning in various computer vision tasks, are usually considered as impractical for online visual tracking, because they require very long training time and a large number of training samples. In this paper, we present an efficient and very robust tracking algorithm using a single convolutional neural network (CNN) for learning effective feature representations of the target object in a purely online manner. Our contributions are multifold. First, we introduce a novel truncated structural loss function that maintains as many training samples as possible and reduces the risk of tracking error accumulation. Second, we enhance the ordinary stochastic gradient descent approach in CNN training with a robust sample selection mechanism. The sampling mechanism randomly generates positive and negative samples from different temporal distributions, which are generated by taking the temporal relations and label noise into account. Finally, a lazy yet effective updating scheme is designed for CNN training. Equipped with this novel updating algorithm, the CNN model is robust to some long-existing difficulties in visual tracking, such as occlusion or incorrect detections, without loss of the effective adaption for significant appearance changes. In the experiment, our CNN tracker outperforms all compared state-of-the-art methods on two recently proposed benchmarks, which in total involve over 60 video sequences. The remarkable performance improvement over the existing trackers illustrates the superiority of the feature representations, which are learned purely online via the proposed deep learning framework.

Connected through hyperedge HE-1 (induced by source paper "high performance visual object tracking with unified convolutional networks"):
  with members:
    - "exploiting offset guided network for pose estimation and tracking"
    - "state aware re identification feature for multi target multi camera tracking"
    - "fastpose towards real time pose estimation and tracking via scale normalized multi task networks"
    - "optical flow based real time moving object detection in unconstrained scenes"
    - "optical flow based online moving foreground analysis"
    - "action machine rethinking action recognition in trimmed videos"
    - "distractor aware siamese networks for visual object tracking"
    - "an efficient optical flow based motion detection method for non stationary scenes"

Connected through hyperedge HE-2 (induced by source paper "human following for wheeled robot with monocular pan tilt camera"):
  with members:
    - "detection and tracking of small objects in sparse 3d laser range data"
    - "exploiting offset guided network for pose estimation and tracking"
    - "state aware re identification feature for multi target multi camera tracking"
    - "fastpose towards real time pose estimation and tracking via scale normalized multi task networks"
    - "optical flow based real time moving object detection in unconstrained scenes"
    - "action machine rethinking action recognition in trimmed videos"
    - "attention guided unified network for panoptic segmentation"
    - "an efficient optical flow based motion detection method for non stationary scenes"

Connected through hyperedge HE-3 (induced by source paper "graph neural based end to end data association framework for online multiple object tracking"):
  with members:
    - "online multi object tracking with dual matching attention networks"
    - "relational deep reinforcement learning"
    - "features for multi target multi camera tracking and re identification"
    - "a twofold siamese network for real time object tracking"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "model free tracking with deep appearance and motion features integration"
    - "relational inductive biases deep learning and graph networks"
    - "semi supervised learning on graphs with generative adversarial nets"

Connected through hyperedge HE-4 (induced by source paper "spstracker sub peak suppression of response map for robust object tracking"):
  with members:
    - "foveabox beyond anchor based object detector"
    - "centernet keypoint triplets for object detection"
    - "deeper and wider siamese networks for real time visual tracking"
    - "cornernet detecting objects as paired keypoints"
    - "atom accurate tracking by overlap maximization"
    - "distractor aware siamese networks for visual object tracking"
    - "a twofold siamese network for real time object tracking"
    - "fast online object tracking and segmentation a unifying approach"

Connected through hyperedge HE-5 (induced by source paper "real time visual tracking using spatial aware temporal aggregation network"):
  with members:
    - "object detection in video with spatiotemporal sampling networks"
    - "efficient video object segmentation via network modulation"
    - "lasot a high quality benchmark for large scale single object tracking"
    - "good features to correlate for visual tracking"
    - "unveiling the power of deep tracking"
    - "vital visual tracking via adversarial learning"
    - "dcfnet discriminant correlation filters network for visual tracking"
    - "end to end representation learning for correlation filter based tracking"

Connected through hyperedge HE-6 (induced by source paper "dawn dual augmented memory network for unsupervised video object tracking"):
  with members:
    - "distractor aware siamese networks for visual object tracking"
    - "learning dynamic memory networks for object tracking"
    - "memory matching networks for one shot image recognition"
    - "real time visual tracking by deep reinforced decision making"
    - "mavot memory augmented video object tracking"
    - "deep reinforcement learning for visual object tracking in videos"
    - "online multi object tracking using cnn based single object tracker with spatial temporal attention mechanism"
    - "multi context attention for human pose estimation"

Connected through hyperedge HE-7 (induced by source paper "rethinking convolutional features in correlation filter based tracking"):
  with members:
    - "context aware deep feature compression for high speed visual tracking"
    - "a twofold siamese network for real time object tracking"
    - "deep attentive tracking via reciprocative learning"
    - "learning spatial aware regressions for visual tracking"
    - "dual deep network for visual tracking"
    - "robust and real time deep tracking via multi scale domain adaptation"
    - "a novel performance evaluation methodology for single target trackers"
    - "sanet structure aware network for visual tracking"

Connected through hyperedge HE-8 (induced by source paper "detection and tracking of multiple mice using part proposal networks"):
  with members:
    - "real time multiple people tracking with deeply learned candidate selection and person re identification"
    - "mask r cnn"
    - "convolutional pose machines"
    - "simple online and realtime tracking"
    - "ssd single shot multibox detector"
    - "deepercut a deeper stronger and faster multi person pose estimation model"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "you only look once unified real time object detection"

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)