human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "efficient object detection in large images using deep reinforcement learning"):
  Abstract: Traditionally, an object detector is applied to every part of the scene of interest, and its accuracy and computational cost increases with higher resolution images. However, in some application domains such as remote sensing, purchasing high spatial resolution images is expensive. To reduce the large computational and monetary cost associated with using high spatial resolution images, we propose a reinforcement learning agent that adaptively selects the spatial resolution of each image that is provided to the detector. In particular, we train the agent in a dual reward setting to choose low spatial resolution images to be run through a coarse level detector when the image is dominated by large objects, and high spatial resolution images to be run through a fine level detector when it is dominated by small objects. This reduces the dependency on high spatial resolution images for building a robust detector and increases run-time efficiency. We perform experiments on the xView dataset, consisting of large images, where we increase run-time efficiency by 50% and use high resolution images only 30% of the time while maintaining similar accuracy as a detector that uses only high resolution images.

Includes members:
  Member "predicting economic development using geolocated wikipedia articles":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning when and where to zoom with deep reinforcement learning")
  Member "reduced focal loss 1st place solution to xview object detection in satellite imagery":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsupervised feature learning in remote sensing")
  Member "learning to interpret satellite images in global scale using wikipedia":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning when and where to zoom with deep reinforcement learning")
  Member "using pre training can improve model robustness and uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarial distributional training for robust deep learning")
  Member "learning to interpret satellite images using wikipedia":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning when and where to zoom with deep reinforcement learning")
  Member "xview objects in context in overhead imagery":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generating interpretable poverty maps using object detection in satellite images")
  Member "autofocus efficient multi scale inference":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "panda a gigapixel level human centric video dataset")
  Member "yolov3 an incremental improvement":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")

Question: Please tell me which class the source paper 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)