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 "playing for benchmarks"):
  Abstract: We present a benchmark suite for visual perception. The benchmark is based on more than 250K high-resolution video frames, all annotated with ground-truth data for both low-level and high-level vision tasks, including optical flow, semantic instance segmentation, object detection and tracking, object-level 3D scene layout, and visual odometry. Ground-truth data for all tasks is available for every frame. The data was collected while driving, riding, and walking a total of 184 kilometers in diverse ambient conditions in a realistic virtual world. To create the benchmark, we have developed a new approach to collecting ground-truth data from simulated worlds without access to their source code or content. We conduct statistical analyses that show that the composition of the scenes in the benchmark closely matches the composition of corresponding physical environments. The realism of the collected data is further validated via perceptual experiments. We analyze the performance of state-of-the-art methods for multiple tasks, providing reference baselines and highlighting challenges for future research. The supplementary video can be viewed at this https URL

Includes members:
  Member "photographic image synthesis with cascaded refinement networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi channel attention selection gans for guided image to image translation")
  Member "unrealcv connecting computer vision to unreal engine":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the synthinel 1 dataset a collection of high resolution synthetic overhead imagery for building segmentation")
  Member "the cityscapes dataset for semantic urban scene understanding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "flownet 2 0 evolution of optical flow estimation with deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning video object segmentation from unlabeled videos")
  Member "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "boundary aware instance segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep multicameral decoding for localizing unoccluded object instances from a single rgb image")
  Member "ubernet training a universal convolutional neural network for low mid and high level vision using diverse datasets and limited memory":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mti net multi scale task interaction networks for multi task learning")
  Member "cross stitch networks for multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mti net multi scale task interaction networks for multi task learning")

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)