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: "distractor aware siamese networks for visual object tracking"
  Abstract: Recently, Siamese networks have drawn great attention in visual tracking community because of their balanced accuracy and speed. However, features used in most Siamese tracking approaches can only discriminate foreground from the non-semantic backgrounds. The semantic backgrounds are always considered as distractors, which hinders the robustness of Siamese trackers. In this paper, we focus on learning distractor-aware Siamese networks for accurate and long-term tracking. To this end, features used in traditional Siamese trackers are analyzed at first. We observe that the imbalanced distribution of training data makes the learned features less discriminative. During the off-line training phase, an effective sampling strategy is introduced to control this distribution and make the model focus on the semantic distractors. During inference, a novel distractor-aware module is designed to perform incremental learning, which can effectively transfer the general embedding to the current video domain. In addition, we extend the proposed approach for long-term tracking by introducing a simple yet effective local-to-global search region strategy. Extensive experiments on benchmarks show that our approach significantly outperforms the state-of-the-arts, yielding 9.6% relative gain in VOT2016 dataset and 35.9% relative gain in UAV20L dataset. The proposed tracker can perform at 160 FPS on short-term benchmarks and 110 FPS on long-term benchmarks.

Connected through hyperedge HE-1 (induced by source paper "siamese attentional keypoint network for high performance visual tracking"):
  with members:
    - "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"
    - "learning reinforced attentional representation for end to end visual tracking"
    - "learning cascaded siamese networks for high performance visual tracking"
    - "deeper and wider siamese networks for real time visual tracking"
    - "cornernet detecting objects as paired keypoints"
    - "large margin structured convolution operator for thermal infrared object tracking"
    - "efficient multi level correlating for visual tracking"
    - "high performance visual tracking with circular and structural operators"

Connected through hyperedge HE-2 (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-3 (induced by source paper "object adaptive lstm network for real time visual tracking with adversarial data augmentation"):
  with members:
    - "quadruplet network with one shot learning for fast visual object tracking"
    - "deep visual attention prediction"
    - "context aware deep feature compression for high speed visual tracking"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "video salient object detection via fully convolutional networks"
    - "vital visual tracking via adversarial learning"
    - "learning dynamic memory networks for object tracking"
    - "recurrent filter learning for visual tracking"

Connected through hyperedge HE-4 (induced by source paper "spark spatial aware online incremental attack against visual tracking"):
  with members:
    - "physical adversarial textures that fool visual object tracking"
    - "roi pooled correlation filters for visual tracking"
    - "deeper and wider siamese networks for real time visual tracking"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "seeing isn t believing practical adversarial attack against object detectors"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "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 "multi drone based single object tracking with agent sharing network"):
  with members:
    - "joint group feature selection and discriminative filter learning for robust visual object tracking"
    - "vision meets drones a challenge"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "features for multi target multi camera tracking and re identification"
    - "lasot a high quality benchmark for large scale single object tracking"
    - "the unmanned aerial vehicle benchmark object detection and tracking"
    - "siamrpn evolution of siamese visual tracking with very deep networks"
    - "trackingnet a large scale dataset and benchmark for object tracking in the wild"

Connected through hyperedge HE-6 (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"
    - "target aware deep tracking"
    - "hybrid macro micro level backpropagation for training deep spiking neural networks"
    - "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-7 (induced by source paper "aggregation signature for small object tracking"):
  with members:
    - "circulant binary convolutional networks enhancing the performance of 1 bit dcnns with circulant back propagation"
    - "state aware anti drift object tracking"
    - "latent constrained correlation filter"
    - "the world of fast moving objects"
    - "robust estimation of similarity transformation for visual object tracking"
    - "eco efficient convolution operators for tracking"
    - "discriminative scale space tracking"
    - "learning spatially regularized correlation filters for visual tracking"

Connected through hyperedge HE-8 (induced by source paper "pose assisted multi camera collaboration for active object tracking"):
  with members:
    - "actor critic policy optimization in partially observable multiagent environments"
    - "context aware deep feature compression for high speed visual tracking"
    - "virtual to real learning to control in visual semantic segmentation"
    - "end to end active object tracking and its real world deployment via reinforcement learning"
    - "siamrpn evolution of siamese visual tracking with very deep networks"
    - "end to end active object tracking via reinforcement learning"
    - "learning to track at 100 fps with deep regression networks"

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)