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: "hierarchical object detection with deep reinforcement learning"
  Abstract: We present a method for performing hierarchical object detection in images guided by a deep reinforcement learning agent. The key idea is to focus on those parts of the image that contain richer information and zoom on them. We train an intelligent agent that, given an image window, is capable of deciding where to focus the attention among five different predefined region candidates (smaller windows). This procedure is iterated providing a hierarchical image analysis.We compare two different candidate proposal strategies to guide the object search: with and without overlap. Moreover, our work compares two different strategies to extract features from a convolutional neural network for each region proposal: a first one that computes new feature maps for each region proposal, and a second one that computes the feature maps for the whole image to later generate crops for each region proposal. Experiments indicate better results for the overlapping candidate proposal strategy and a loss of performance for the cropped image features due to the loss of spatial resolution. We argue that, while this loss seems unavoidable when working with large amounts of object candidates, the much more reduced amount of region proposals generated by our reinforcement learning agent allows considering to extract features for each location without sharing convolutional computation among regions.

Connected through hyperedge HE-1 (induced by source paper "robust multimodal image registration using deep recurrent reinforcement learning"):
  with members:
    - "autonomous driving in reality with reinforcement learning and image translation"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "end to end unsupervised deformable image registration with a convolutional neural network"
    - "a survey on deep learning in medical image analysis"
    - "end to end active object tracking via reinforcement learning"
    - "an artificial agent for robust image registration"
    - "a deep metric for multimodal registration"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "multi stage reinforcement learning for object detection"):
  with members:
    - "fast r cnn"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "spatial memory for context reasoning in object detection"):
  with members:
    - "an implementation of faster rcnn with study for region sampling"
    - "neural map structured memory for deep reinforcement learning"
    - "deep variation structured reinforcement learning for visual relationship and attribute detection"
    - "cognitive mapping and planning for visual navigation"
    - "end to end instance segmentation and counting with recurrent attention"
    - "ssd single shot multibox detector"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-4 (induced by source paper "saliency based sequential image attention with multiset prediction"):
  with members:
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "feudal networks for hierarchical reinforcement learning"
    - "paying more attention to saliency image captioning with saliency and context attention"
    - "towards instance segmentation with object priority prominent object detection and recognition"
    - "hierarchical attention networks"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "localizing by describing attribute guided attention localization for fine grained recognition"
    - "generating visual explanations"

Connected through hyperedge HE-5 (induced by source paper "deep reinforcement learning attention selection for person re identification"):
  with members:
    - "improved image captioning via policy gradient optimization of spider"
    - "person re identification in the wild"
    - "learning a discriminative null space for person re identification"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "learning deep embeddings with histogram loss"
    - "end to end deep learning for person search"
    - "self critical sequence training for image captioning"
    - "deep feature learning with relative distance comparison for person re identification"

Connected through hyperedge HE-6 (induced by source paper "a self adaptive proposal model for temporal action detection based on reinforcement learning"):
  with members:
    - "turn tap temporal unit regression network for temporal action proposals"
    - "tree structured reinforcement learning for sequential object localization"
    - "a pursuit of temporal accuracy in general activity detection"
    - "temporal action localization in untrimmed videos via multi stage cnns"
    - "efficient action detection in untrimmed videos via multi task learning"
    - "end to end learning of action detection from frame glimpses in videos"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "active object localization with deep reinforcement learning"

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)