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: "re3 real time recurrent regression networks for visual tracking of generic objects"
  Abstract: Robust object tracking requires knowledge and understanding of the object being tracked: its appearance, its motion, and how it changes over time. A tracker must be able to modify its underlying model and adapt to new observations. We present Re3, a real-time deep object tracker capable of incorporating temporal information into its model. Rather than focusing on a limited set of objects or training a model at test-time to track a specific instance, we pretrain our generic tracker on a large variety of objects and efficiently update on the fly; Re3 simultaneously tracks and updates the appearance model with a single forward pass. This lightweight model is capable of tracking objects at 150 FPS, while attaining competitive results on challenging benchmarks. We also show that our method handles temporary occlusion better than other comparable trackers using experiments that directly measure performance on sequences with occlusion.

Connected through hyperedge HE-1 (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"
    - "distractor aware siamese networks for visual object tracking"
    - "video salient object detection via fully convolutional networks"
    - "vital visual tracking via adversarial learning"
    - "learning dynamic memory networks for object tracking"

Connected through hyperedge HE-2 (induced by source paper "got 10k a large high diversity benchmark for generic object tracking in the wild"):
  with members:
    - "ptb tir a thermal infrared pedestrian tracking benchmark"
    - "class balanced loss based on effective number of samples"
    - "deeper and wider siamese networks for real time visual tracking"
    - "real time mdnet"
    - "long term tracking in the wild a benchmark"
    - "fast and accurate online video object segmentation via tracking parts"
    - "a twofold siamese network for real time object tracking"
    - "meta tracker fast and robust online adaptation for visual object trackers"

Connected through hyperedge HE-3 (induced by source paper "learning to update for object tracking with recurrent meta learner"):
  with members:
    - "good features to correlate for visual tracking"
    - "recurrent filter learning for visual tracking"
    - "end to end representation learning for correlation filter based tracking"
    - "hierarchical attentive recurrent tracking"
    - "learning spatial aware regressions for visual tracking"
    - "youtube boundingboxes a large high precision human annotated data set for object detection in video"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "crest convolutional residual learning for visual tracking"

Connected through hyperedge HE-4 (induced by source paper "end to end recurrent multi object tracking and trajectory prediction with relational reasoning"):
  with members:
    - "what the constant velocity model can teach us about pedestrian motion prediction"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "deep sets"
    - "end to end representation learning for correlation filter based tracking"
    - "hierarchical attentive recurrent tracking"

Connected through hyperedge HE-5 (induced by source paper "a single target tracking algorithm based on generative adversarial networks"):
  with members:
    - "deeper and wider siamese networks for real time visual tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "lasot a high quality benchmark for large scale single object tracking"
    - "siamrpn evolution of siamese visual tracking with very deep networks"
    - "end to end representation learning for correlation filter based tracking"
    - "end to end flow correlation tracking with spatial temporal attention"
    - "eco efficient convolution operators for tracking"

Connected through hyperedge HE-6 (induced by source paper "meta tracker fast and robust online adaptation for visual object trackers"):
  with members:
    - "recurrent filter learning for visual tracking"
    - "end to end representation learning for correlation filter based tracking"
    - "learned optimizers that scale and generalize"
    - "continuous adaptation via meta learning in nonstationary and competitive environments"
    - "meta sgd learning to learn quickly for few shot learning"
    - "crest convolutional residual learning for visual tracking"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "tracking as online decision making learning a policy from streaming videos with reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "dsnet deep and shallow feature learning for efficient visual tracking"):
  with members:
    - "good features to correlate for visual tracking"
    - "dcfnet discriminant correlation filters network for visual tracking"
    - "end to end representation learning for correlation filter based tracking"
    - "crest convolutional residual learning for visual tracking"
    - "eco efficient convolution operators for tracking"
    - "fully convolutional siamese networks for object tracking"
    - "ssd single shot multibox detector"
    - "hypernet towards accurate region proposal generation and joint object detection"

Connected through hyperedge HE-8 (induced by source paper "differentiating objects by motion joint detection and tracking of small flying objects"):
  with members:
    - "t cnn tubelets with convolutional neural networks for object detection from videos"
    - "flow guided feature aggregation for video object detection"
    - "perceptual generative adversarial networks for small object detection"
    - "detect to track and track to detect"
    - "deep tracking seeing beyond seeing using recurrent neural networks"
    - "object detection from video tubelets with convolutional neural networks"
    - "online multi target tracking using recurrent neural networks"
    - "is faster r cnn doing well for pedestrian 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)