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: "real time visual tracking by deep reinforced decision making"
  Abstract: One of the major challenges of model-free visual tracking problem has been the difficulty originating from the unpredictable and drastic changes in the appearance of objects we target to track. Existing methods tackle this problem by updating the appearance model on-line in order to adapt to the changes in the appearance. Despite the success of these methods however, inaccurate and erroneous updates of the appearance model result in a tracker drift. In this paper, we introduce a novel real-time visual tracking algorithm based on a template selection strategy constructed by deep reinforcement learning methods. The tracking algorithm utilizes this strategy to choose the appropriate template for tracking a given frame. The template selection strategy is self-learned by utilizing a simple policy gradient method on numerous training episodes randomly generated from a tracking benchmark dataset. Our proposed reinforcement learning framework is generally applicable to other confidence map based tracking algorithms. The experiment shows that our tracking algorithm runs in real-time speed of 43 fps and the proposed policy network effectively decides the appropriate template for successful visual tracking.

Connected through hyperedge HE-1 (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"
    - "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"
    - "residual attention network for image classification"

Connected through hyperedge HE-2 (induced by source paper "learning q network for active information acquisition"):
  with members:
    - "end to end representation learning for correlation filter based tracking"
    - "bridging the gap between value and policy based reinforcement learning"
    - "end to end active object tracking via reinforcement learning"
    - "deep reinforcement learning for visual object tracking in videos"
    - "assumed density filtering q learning"
    - "deep tracking seeing beyond seeing using recurrent neural networks"
    - "online multi target tracking using recurrent neural networks"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"

Connected through hyperedge HE-3 (induced by source paper "handcrafted and deep trackers a review of recent object tracking approaches"):
  with members:
    - "adaptive correlation filters with long term and short term memory for object 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"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "tracking the trackers an analysis of the state of the art in multiple object tracking"
    - "dual deep network for visual tracking"
    - "large margin object tracking with circulant feature maps"

Connected through hyperedge HE-4 (induced by source paper "end to end active object tracking and its real world deployment via reinforcement learning"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "yolov3 an incremental improvement"
    - "building generalizable agents with a realistic and rich 3d environment"
    - "end to end representation learning for correlation filter based tracking"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "transferring end to end visuomotor control from simulation to real world for a multi stage task"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "sim2real view invariant visual servoing by recurrent control"

Connected through hyperedge HE-5 (induced by source paper "correlation filter selection for visual tracking using reinforcement learning"):
  with members:
    - "dcfnet discriminant correlation filters network for visual tracking"
    - "end to end representation learning for correlation filter based tracking"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "end to end active object tracking via reinforcement learning"
    - "large margin object tracking with circulant feature maps"
    - "tree structured reinforcement learning for sequential object localization"
    - "deep reinforcement learning for visual object tracking in videos"
    - "multi shot pedestrian re identification via sequential decision making"

Connected through hyperedge HE-6 (induced by source paper "end to end active object tracking via reinforcement learning"):
  with members:
    - "learning policies for adaptive tracking with deep feature cascades"
    - "dual deep network for visual tracking"
    - "tree structured reinforcement learning for sequential object localization"
    - "deep reinforcement learning for visual object tracking in videos"
    - "tracking as online decision making learning a policy from streaming videos with reinforcement learning"
    - "vizdoom a doom based ai research platform for visual reinforcement learning"
    - "cad2rl real single image flight without a single real image"
    - "siamese instance search for tracking"

Connected through hyperedge HE-7 (induced by source paper "tracking for half an hour"):
  with members:
    - "siamese instance search for tracking"
    - "fully convolutional siamese networks for object tracking"
    - "learning multi domain convolutional neural networks for visual tracking"
    - "staple complementary learners for real time tracking"
    - "very deep convolutional networks for large scale image recognition"

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)