human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "end to end active object tracking via reinforcement learning"):
  Abstract: We study active object tracking, where a tracker takes as input the visual observation (i.e., frame sequence) and produces the camera control signal (e.g., move forward, turn left, etc.). Conventional methods tackle the tracking and the camera control separately, which is challenging to tune jointly. It also incurs many human efforts for labeling and many expensive trial-and-errors in realworld. To address these issues, we propose, in this paper, an end-to-end solution via deep reinforcement learning, where a ConvNet-LSTM function approximator is adopted for the direct frame-toaction prediction. We further propose an environment augmentation technique and a customized reward function, which are crucial for a successful training. The tracker trained in simulators (ViZDoom, Unreal Engine) shows good generalization in the case of unseen object moving path, unseen object appearance, unseen background, and distracting object. It can restore tracking when occasionally losing the target. With the experiments over the VOT dataset, we also find that the tracking ability, obtained solely from simulators, can potentially transfer to real-world scenarios.

Includes members:
  Member "real time visual tracking by deep reinforced decision making":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dawn dual augmented memory network for unsupervised video object tracking")
  Member "learning policies for adaptive tracking with deep feature cascades":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "object adaptive lstm network for real time visual tracking with adversarial data augmentation")
  Member "dual deep network for visual tracking":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "siamese attentional keypoint network for high performance visual tracking")
  Member "tree structured reinforcement learning for sequential object localization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pixelrl fully convolutional network with reinforcement learning for image processing")
  Member "deep reinforcement learning for visual object tracking in videos":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep reinforcement learning for active human pose estimation")
  Member "tracking as online decision making learning a policy from streaming videos with reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "looking fast and slow memory guided mobile video object detection")
  Member "vizdoom a doom based ai research platform for visual reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the sensory commutativity of action sequences for embodied agents")
  Member "cad2rl real single image flight without a single real image":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "increasing generality in machine learning through procedural content generation")

Question: Please tell me which class the source paper 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)