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: "target aware deep tracking"
  Abstract: Existing deep trackers mainly use convolutional neural networks pre-trained for generic object recognition task for representations. Despite demonstrated successes for numerous vision tasks, the contributions of using pre-trained deep features for visual tracking are not as significant as that for object recognition. The key issue is that in visual tracking the targets of interest can be arbitrary object class with arbitrary forms. As such, pre-trained deep features are less effective in modeling these targets of arbitrary forms for distinguishing them from the background. In this paper, we propose a novel scheme to learn target-aware features, which can better recognize the targets undergoing significant appearance variations than pre-trained deep features. To this end, we develop a regression loss and a ranking loss to guide the generation of target-active and scale-sensitive features. We identify the importance of each convolutional filter according to the back-propagated gradients and select the target-aware features based on activations for representing the targets. The target-aware features are integrated with a Siamese matching network for visual tracking. Extensive experimental results show that the proposed algorithm performs favorably against the state-of-the-art methods in terms of accuracy and speed.

Connected through hyperedge HE-1 (induced by source paper "siamese box adaptive network for visual tracking"):
  with members:
    - "learning discriminative model prediction for tracking"
    - "reppoints point set representation for object detection"
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "gradnet gradient guided network for visual object tracking"
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "spm tracker series parallel matching for real time visual object tracking"
    - "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"
    - "feature selective anchor free module for single shot object detection"

Connected through hyperedge HE-2 (induced by source paper "template based unseen instance detection"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "deceptionnet network driven domain randomization"
    - "learning to track any object"
    - "on the convergence of adam and beyond"
    - "photorealistic image synthesis for object instance detection"
    - "deep model based 6d pose refinement in rgb"
    - "bop benchmark for 6d object pose estimation"
    - "deep object pose estimation for semantic robotic grasping of household objects"

Connected through hyperedge HE-3 (induced by source paper "siamsnn spike based siamese network for energy efficient and real time object tracking"):
  with members:
    - "deep learning in spiking neural networks"
    - "spiking yolo spiking neural network for energy efficient object detection"
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "distractor aware siamese networks for visual object tracking"
    - "fast and efficient information transmission with burst spikes in deep spiking neural networks"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "quantization mimic towards very tiny cnn for object detection"
    - "siamrpn evolution of siamese visual tracking with very deep networks"

Connected through hyperedge HE-4 (induced by source paper "domainsiam domain aware siamese network for visual object tracking"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "atom accurate tracking by overlap maximization"
    - "denssiam end to end densely siamese network with self attention model for object tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "a twofold siamese network for real time object tracking"
    - "lasot a high quality benchmark for large scale single object tracking"
    - "fast online object tracking and segmentation a unifying approach"

Connected through hyperedge HE-5 (induced by source paper "dense feature aggregation and pruning for rgbt tracking"):
  with members:
    - "deeper and wider siamese networks for real time visual tracking"
    - "real time mdnet"
    - "rgb t object tracking benchmark and baseline"
    - "deep attentive tracking via reciprocative learning"
    - "domain generalization with domain specific aggregation modules"
    - "siamrpn evolution of siamese visual tracking with very deep networks"
    - "end to end representation learning for correlation filter based tracking"
    - "deep layer aggregation"

Connected through hyperedge HE-6 (induced by source paper "exact adversarial attack to image captioning via structured output learning with latent variables"):
  with members:
    - "tencent ml images a large scale multi label image database for visual representation learning"
    - "tagging like humans diverse and distinct image annotation"
    - "physical adversarial examples for object detectors"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "attacking visual language grounding with adversarial examples a case study on neural image captioning"
    - "on the robustness of semantic segmentation models to adversarial attacks"
    - "adversarial examples for semantic segmentation and object detection"

Connected through hyperedge HE-7 (induced by source paper "augmented memory for correlation filters in real time uav tracking"):
  with members:
    - "can a robot become a movie director learning artistic principles for aerial cinematography"
    - "learning aberrance repressed correlation filters for real time uav tracking"
    - "towards a robust aerial cinematography platform localizing and tracking moving targets in unstructured environments"
    - "boundary effect aware visual tracking for uav with online enhanced background learning and multi frame consensus verification"
    - "the unmanned aerial vehicle benchmark object detection and tracking"
    - "learning spatial temporal regularized correlation filters for visual tracking"
    - "kernel cross correlator"
    - "integrating boundary and center correlation filters for visual tracking with aspect ratio variation"

Connected through hyperedge HE-8 (induced by source paper "multi task driven feature models for thermal infrared tracking"):
  with members:
    - "ptb tir a thermal infrared pedestrian tracking benchmark"
    - "learning deep multi level similarity for thermal infrared object tracking"
    - "synthetic data generation for end to end thermal infrared tracking"
    - "deeper and wider siamese networks for real time visual tracking"
    - "large margin structured convolution operator for thermal infrared object tracking"
    - "a twofold siamese network for real time object tracking"
    - "vital visual tracking via adversarial learning"
    - "learning spatial temporal regularized correlation filters for visual 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)