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 "large scale interactive object segmentation with human annotators"):
  Abstract: Manually annotating object segmentation masks is very time consuming. Interactive object segmentation methods offer a more efficient alternative where a human annotator and a machine segmentation model collaborate. In this paper we make several contributions to interactive segmentation: (1) we systematically explore in simulation the design space of deep interactive segmentation models and report new insights and caveats; (2) we execute a large-scale annotation campaign with real human annotators, producing masks for 2.5M instances on the OpenImages dataset. We plan to release this data publicly, forming the largest existing dataset for instance segmentation. Moreover, by re-annotating part of the COCO dataset, we show that we can produce instance masks 3 times faster than traditional polygon drawing tools while also providing better quality. (3) We present a technique for automatically estimating the quality of the produced masks which exploits indirect signals from the annotation process.

Includes members:
  Member "deepigeos a deep interactive geodesic framework for medical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on active learning and human in the loop deep learning for medical image analysis")
  Member "the apolloscape open dataset for autonomous driving and its application":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "weighted average precision adversarial example detection in the visual perception of autonomous vehicles")
  Member "weakly supervised instance segmentation using class peak response":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "weakly supervised instance segmentation by deep community learning")
  Member "efficient interactive annotation of segmentation datasets with polygon rnn":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "block annotation better image annotation for semantic segmentation with sub image decomposition")
  Member "a fully convolutional two stream fusion network for interactive image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "getting to 99 accuracy in interactive segmentation")
  Member "iteratively trained interactive segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "getting to 99 accuracy in interactive segmentation")
  Member "the open images dataset v4 unified image classification object detection and visual relationship detection at scale":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "revisiting the sibling head in object detector")
  Member "revisiting unreasonable effectiveness of data in deep learning era":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "rethinking the hyperparameters for fine tuning")

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)