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: "deep reinforcement learning for visual object tracking in videos"
  Abstract: In this paper we introduce a fully end-to-end approach for visual tracking in videos that learns to predict the bounding box locations of a target object at every frame. An important insight is that the tracking problem can be considered as a sequential decision-making process and historical semantics encode highly relevant information for future decisions. Based on this intuition, we formulate our model as a recurrent convolutional neural network agent that interacts with a video overtime, and our model can be trained with reinforcement learning (RL) algorithms to learn good tracking policies that pay attention to continuous, inter-frame correlation and maximize tracking performance in the long run. The proposed tracking algorithm achieves state-of-the-art performance in an existing tracking benchmark and operates at frame-rates faster than real-time. To the best of our knowledge, our tracker is the first neural-network tracker that combines convolutional and recurrent networks with RL algorithms.

Connected through hyperedge HE-1 (induced by source paper "deep reinforcement learning for active human pose estimation"):
  with members:
    - "ordinal depth supervision for 3d human pose estimation"
    - "geometry aware recurrent neural networks for active visual recognition"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "gibson env real world perception for embodied agents"
    - "reinforcement learning of active vision for manipulating objects under occlusions"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "end to end recovery of human shape and pose"
    - "a dataset for developing and benchmarking active vision"

Connected through hyperedge HE-2 (induced by source paper "weakly supervised video summarization by hierarchical reinforcement learning"):
  with members:
    - "rethinking the evaluation of video summaries"
    - "fully convolutional network with multi step reinforcement learning for image processing"
    - "crafting a toolchain for image restoration by deep reinforcement learning"
    - "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward"
    - "feudal networks for hierarchical reinforcement learning"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "deep reinforcement learning with a combinatorial action space for predicting popular reddit threads"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"

Connected through hyperedge HE-3 (induced by source paper "anomaly detection in road traffic using visual surveillance a survey"):
  with members:
    - "stan spatio temporal adversarial networks for abnormal event detection"
    - "real world anomaly detection in surveillance videos"
    - "review on computer vision techniques in emergency situations"
    - "robust deep and inductive anomaly detection"
    - "abnormal event detection in videos using generative adversarial nets"
    - "video captioning via hierarchical reinforcement learning"
    - "unmasking the abnormal events in video"
    - "joint detection and recounting of abnormal events by learning deep generic knowledge"

Connected through hyperedge HE-4 (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"
    - "real time visual tracking by deep reinforced decision making"
    - "mavot memory augmented video object tracking"
    - "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-5 (induced by source paper "deep learning acceleration techniques for real time mobile vision applications"):
  with members:
    - "convolutional neural network for intrusion detection system in cyber physical systems"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "towards high performance video object detection for mobiles"
    - "ai benchmark running deep neural networks on android smartphones"
    - "towards a theoretical understanding of hashing based neural nets"
    - "tiny ssd a tiny single shot detection deep convolutional neural network for real time embedded object detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "an efficient deep learning hashing neural network for mobile visual search"

Connected through hyperedge HE-6 (induced by source paper "quadruplet network with one shot learning for fast visual object tracking"):
  with members:
    - "end to end representation learning for correlation filter based tracking"
    - "learning policies for adaptive tracking with deep feature cascades"
    - "large margin object tracking with circulant feature maps"
    - "parallel tracking and verifying a framework for real time and high accuracy visual tracking"
    - "learning background aware correlation filters for visual tracking"
    - "learning feed forward one shot learners"
    - "fully convolutional siamese networks for object tracking"
    - "beyond correlation filters learning continuous convolution operators for visual tracking"

Connected through hyperedge HE-7 (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"
    - "end to end active object tracking via reinforcement learning"
    - "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-8 (induced by source paper "real time tracker with fast recovery from target loss"):
  with members:
    - "long term tracking in the wild a benchmark"
    - "end to end representation learning for correlation filter based tracking"
    - "tracking for half an hour"
    - "eco efficient convolution operators for tracking"
    - "fully convolutional siamese networks for object tracking"
    - "beyond correlation filters learning continuous convolution operators for visual tracking"
    - "learning multi domain convolutional neural networks for visual tracking"
    - "high speed tracking with kernelized correlation filters"

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)