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 "cut paste and learn surprisingly easy synthesis for instance detection"):
  Abstract: A major impediment in rapidly deploying object detection models for instance detection is the lack of large annotated datasets. For example, finding a large labeled dataset containing instances in a particular kitchen is unlikely. Each new environment with new instances requires expensive data collection and annotation. In this paper, we propose a simple approach to generate large annotated instance datasets with minimal effort. Our key insight is that ensuring only patch-level realism provides enough training signal for current object detector models. We automatically `cut' object instances and `paste' them on random backgrounds. A naive way to do this results in pixel artifacts which result in poor performance for trained models. We show how to make detectors ignore these artifacts during training and generate data that gives competitive performance on real data. Our method outperforms existing synthesis approaches and when combined with real images improves relative performance by more than 21% on benchmark datasets. In a cross-domain setting, our synthetic data combined with just 10% real data outperforms models trained on all real data.

Includes members:
  Member "rendering synthetic objects into legacy photographs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "seeing the world in a bag of chips")
  Member "a self supervised learning system for object detection using physics simulation and multi view pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "template based unseen instance detection")
  Member "segicp integrated deep semantic segmentation and pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "teaser fast and certifiable point cloud registration")
  Member "a dataset for developing and benchmarking active vision":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")
  Member "learning from synthetic humans":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")
  Member "synthesizing training data for object detection in indoor scenes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mixup regularization for region proposal based object detectors")
  Member "how useful is photo realistic rendering for visual learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "adaptive composition gan towards realistic image synthesis")
  Member "ssd single shot multibox detector":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")

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)