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: "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.

Connected through hyperedge HE-1 (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"
    - "distractor aware siamese networks for visual object 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"
    - "learning to track at 100 fps with deep regression networks"

Connected through hyperedge HE-2 (induced by source paper "low light image enhancement algorithm based on retinex and generative adversarial network"):
  with members:
    - "generalized intersection over union a metric and a loss for bounding box regression"
    - "features for multi target multi camera tracking and re identification"
    - "m2det a single shot object detector based on multi level feature pyramid network"
    - "a twofold siamese network for real time object tracking"
    - "yolov3 an incremental improvement"
    - "deep retinex decomposition for low light enhancement"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "learning to see in the dark"

Connected through hyperedge HE-3 (induced by source paper "learning q network for active information acquisition"):
  with members:
    - "end to end representation learning for correlation filter based tracking"
    - "real time visual tracking by deep reinforced decision making"
    - "bridging the gap between value and policy based 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-4 (induced by source paper "bi real net binarizing deep network towards real network performance"):
  with members:
    - "uniq uniform noise injection for the quantization of neural networks"
    - "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"
    - "lq nets learned quantization for highly accurate and compact deep neural networks"
    - "uniq uniform noise injection for non uniform quantization of neural networks"
    - "label refinery improving imagenet classification through label progression"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "deep convolutional neural network inference with floating point weights and fixed point activations"
    - "efficient processing of deep neural networks a tutorial and survey"

Connected through hyperedge HE-5 (induced by source paper "craves controlling robotic arm with a vision based economic system"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "end to end active object tracking and its real world deployment via reinforcement learning"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "the costar block stacking dataset learning with workspace constraints"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"

Connected through hyperedge HE-6 (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"
    - "real time visual tracking by deep reinforced decision making"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "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-7 (induced by source paper "bi real net enhancing the performance of 1 bit cnns with improved representational capability and advanced training algorithm"):
  with members:
    - "deep convolutional neural network inference with floating point weights and fixed point activations"
    - "towards accurate binary convolutional neural network"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "ppr fcn weakly supervised visual relation detection via parallel pairwise r fcn"
    - "visual translation embedding network for visual relation detection"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "learning depth from single monocular images using deep convolutional neural fields"

Connected through hyperedge HE-8 (induced by source paper "perceptual compressive sensing"):
  with members:
    - "neural baby talk"
    - "ista net interpretable optimization inspired deep network for image compressive sensing"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "segflow joint learning for video object segmentation and optical flow"
    - "stylebank an explicit representation for neural image style transfer"
    - "mask r cnn"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "perceptual losses for real time style transfer and super resolution"

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)