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: "tracking as online decision making learning a policy from streaming videos with reinforcement learning"
  Abstract: We formulate tracking as an online decision-making process, where a tracking agent must follow an object despite ambiguous image frames and a limited computational budget. Crucially, the agent must decide where to look in the upcoming frames, when to reinitialize because it believes the target has been lost, and when to update its appearance model for the tracked object. Such decisions are typically made heuristically. Instead, we propose to learn an optimal decision-making policy by formulating tracking as a partially observable decision-making process (POMDP). We learn policies with deep reinforcement learning algorithms that need supervision (a reward signal) only when the track has gone awry. We demonstrate that sparse rewards allow us to quickly train on massive datasets, several orders of magnitude more than past work. Interestingly, by treating the data source of Internet videos as unlimited streams, we both learn and evaluate our trackers in a single, unified computational stream.

Connected through hyperedge HE-1 (induced by source paper "looking fast and slow memory guided mobile video object detection"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "object detection in video with spatiotemporal sampling networks"
    - "depth adaptive computational policies for efficient visual tracking"
    - "towards high performance video object detection for mobiles"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "optimizing video object detection via a scale time lattice"
    - "low latency video semantic segmentation"
    - "t cnn tubelets with convolutional neural networks for object detection from videos"

Connected through hyperedge HE-2 (induced by source paper "pixelrl fully convolutional network with reinforcement learning for image processing"):
  with members:
    - "weakly supervised video summarization by hierarchical reinforcement learning"
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "chainer a deep learning framework for accelerating the research cycle"
    - "fully convolutional network with multi step reinforcement learning for image processing"
    - "ffnet video fast forwarding via reinforcement learning"
    - "distort and recover color enhancement using deep reinforcement learning"
    - "crafting a toolchain for image restoration by deep reinforcement learning"
    - "synthesizing programs for images using reinforced adversarial learning"

Connected through hyperedge HE-3 (induced by source paper "visual tracking by means of deep reinforcement learning and an expert demonstrator"):
  with members:
    - "got 10k a large high diversity benchmark for generic object tracking in the wild"
    - "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"
    - "deeper and wider siamese networks for real time visual tracking"
    - "atom accurate tracking by overlap maximization"
    - "distractor aware siamese networks for visual object tracking"
    - "lasot a high quality benchmark for large scale single object tracking"
    - "learning montezuma s revenge from a single demonstration"
    - "siamrpn evolution of siamese visual tracking with very deep networks"

Connected through hyperedge HE-4 (induced by source paper "skimming perusal tracking a framework for real time and robust long term tracking"):
  with members:
    - "gradnet gradient guided network for visual object tracking"
    - "real time mdnet"
    - "atom accurate tracking by overlap maximization"
    - "long term tracking in the wild a benchmark"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "distractor aware siamese networks for visual object tracking"
    - "correlation tracking via joint discrimination and reliability learning"
    - "fast online object tracking and segmentation a unifying approach"

Connected through hyperedge HE-5 (induced by source paper "imitation learning from imperfect demonstration"):
  with members:
    - "imitation from observation learning to imitate behaviors from raw video via context translation"
    - "positive unlabeled learning with non negative risk estimator"
    - "learning robust rewards with adversarial inverse reinforcement learning"
    - "a deep reinforcement learning chatbot"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "learning to learn from noisy web videos"
    - "learning motion patterns in videos"
    - "generative adversarial imitation learning"

Connected through hyperedge HE-6 (induced by source paper "machine learning beam tracking and weight optimization for mmwave multi uav links"):
  with members:
    - "massive mimo in sub 6 ghz and mmwave physical practical and use case differences"
    - "hybrid beamforming based on implicit channel state information for millimeter wave links"
    - "survey on uav cellular communications practical aspects standardization advancements regulation and security challenges"
    - "hybrid digital and analog beamforming design for large scale antenna arrays"
    - "wireless communications with unmanned aerial vehicles opportunities and challenges"
    - "limited feedback hybrid precoding for multi user millimeter wave systems"
    - "3 d statistical channel model for millimeter wave outdoor mobile broadband communications"
    - "spatially sparse precoding in millimeter wave mimo systems"

Connected through hyperedge HE-7 (induced by source paper "long and short memory balancing in visual co tracking using q learning"):
  with members:
    - "information maximizing sampling to promote tracking by detection"
    - "efficient asymmetric co tracking using uncertainty sampling"
    - "youtube boundingboxes a large high precision human annotated data set for object detection in video"
    - "boltzmann exploration done right"
    - "efficient diverse ensemble for discriminative co tracking"
    - "deep reinforcement learning for visual object tracking in videos"
    - "learning spatially regularized correlation filters for visual tracking"
    - "staple complementary learners for real time tracking"

Connected through hyperedge HE-8 (induced by source paper "an online reinforcement learning approach to quality cost aware task allocation for multi attribute social sensing"):
  with members:
    - "the age of social sensing"
    - "towards reliable online clickbait video detection a content agnostic approach"
    - "zero shot deep reinforcement learning driving policy transfer for autonomous vehicles based on robust control"
    - "online learning for offloading and autoscaling in energy harvesting mobile edge computing"

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)