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: "tree structured reinforcement learning for sequential object localization"
  Abstract: Existing object proposal algorithms usually search for possible object regions over multiple locations and scales separately, which ignore the interdependency among different objects and deviate from the human perception procedure. To incorporate global interdependency between objects into object localization, we propose an effective Tree-structured Reinforcement Learning (Tree-RL) approach to sequentially search for objects by fully exploiting both the current observation and historical search paths. The Tree-RL approach learns multiple searching policies through maximizing the long-term reward that reflects localization accuracies over all the objects. Starting with taking the entire image as a proposal, the Tree-RL approach allows the agent to sequentially discover multiple objects via a tree-structured traversing scheme. Allowing multiple near-optimal policies, Tree-RL offers more diversity in search paths and is able to find multiple objects with a single feed-forward pass. Therefore, Tree-RL can better cover different objects with various scales which is quite appealing in the context of object proposal. Experiments on PASCAL VOC 2007 and 2012 validate the effectiveness of the Tree-RL, which can achieve comparable recalls with current object proposal algorithms via much fewer candidate windows.

Connected through hyperedge HE-1 (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-2 (induced by source paper "efficient object detection in large images using deep reinforcement learning"):
  with members:
    - "predicting economic development using geolocated wikipedia articles"
    - "reduced focal loss 1st place solution to xview object detection in satellite imagery"
    - "learning to interpret satellite images in global scale using wikipedia"
    - "using pre training can improve model robustness and uncertainty"
    - "learning to interpret satellite images using wikipedia"
    - "xview objects in context in overhead imagery"
    - "autofocus efficient multi scale inference"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-3 (induced by source paper "deep q learning driven ct pancreas segmentation with geometry aware u net"):
  with members:
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"
    - "detection aided liver lesion segmentation using deep learning"
    - "a 3d coarse to fine framework for volumetric medical image segmentation"
    - "deformable convolutional networks"
    - "improving automated multiple sclerosis lesion segmentation with a cascaded 3d convolutional neural network approach"
    - "spatial aggregation of holistically nested convolutional neural networks for automated pancreas localization and segmentation"
    - "spatial aggregation of holistically nested networks for automated pancreas segmentation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "counterfactual critic multi agent training for scene graph generation"):
  with members:
    - "knowledge embedded routing network for scene graph generation"
    - "learning to collocate neural modules for image captioning"
    - "scene graph generation with external knowledge and image reconstruction"
    - "graphical contrastive losses for scene graph parsing"
    - "dense relational captioning triple stream networks for relationship based captioning"
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "learning to count objects in natural images for visual question answering"

Connected through hyperedge HE-5 (induced by source paper "autofocus efficient multi scale inference"):
  with members:
    - "cornernet detecting objects as paired keypoints"
    - "optimizing video object detection via a scale time lattice"
    - "beyond trade off accelerate fcn based face detector with higher accuracy"
    - "deformable convnets v2 more deformable better results"
    - "path aggregation network for instance segmentation"
    - "sniper efficient multi scale training"
    - "soft nms improving object detection with one line of code"
    - "fast yolo a fast you only look once system for real time embedded object detection in video"

Connected through hyperedge HE-6 (induced by source paper "a better way to attend attention with trees for video question answering"):
  with members:
    - "tree to sequence attentional neural machine translation"
    - "enhancing and combining sequential and tree lstm for natural language inference"
    - "image captioning with semantic attention"
    - "a focused dynamic attention model for visual question answering"
    - "enhanced lstm for natural language inference"
    - "video fill in the blank with merging lstms"
    - "hierarchical question image co attention for visual question answering"
    - "tgif a new dataset and benchmark on animated gif description"

Connected through hyperedge HE-7 (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"
    - "real time visual tracking by deep reinforced decision making"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "cirl controllable imitative reinforcement learning for vision based self driving"):
  with members:
    - "real to virtual domain unification for end to end autonomous driving"
    - "interpretable learning for self driving cars by visualizing causal attention"
    - "deep reinforcement learning that matters"
    - "virtual to real reinforcement learning for autonomous driving"
    - "parameter space noise for exploration"
    - "fast recurrent fully convolutional networks for direct perception in autonomous driving"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "carla an open urban driving simulator"

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)