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 "joint person segmentation and identification in synchronized first and third person videos"):
  Abstract: In a world in which cameras are becoming more and more pervasive, scenes in public spaces are often captured from multiple perspectives by diverse types of cameras, including surveillance and wearable cameras. An important problem is how to organize these heterogeneous collections of videos by finding connections between them, such as identifying common correspondences between people both appearing in the videos and wearing the cameras. In this paper, we consider scenarios in which multiple cameras of different types are observing a scene involving multiple people, and we wish to solve two specific, related problems: (1) given two or more synchronized third-person videos of a scene, produce a pixel-level segmentation of each visible person and identify corresponding people across different views (i.e., determine who in camera A corresponds with whom in camera B), and (2) given one or more synchronized third-person videos as well as a first-person video taken by a wearable camera, segment and identify the camera wearer in the third-person videos. Unlike previous work which requires ground truth bounding boxes to estimate the correspondences, we jointly perform the person segmentation and identification. We find that solving these two problems simultaneously is mutually beneficial, because better fine-grained segmentations allow us to better perform matching across views, and using information from multiple views helps us perform more accurate segmentation. We evaluate our approach on a challenging dataset of interacting people captured from multiple wearable cameras, and show that our proposed method performs significantly better than the state-of-the-art on both person segmentation and identification.

Includes members:
  Member "encoder decoder with atrous separable convolution for semantic image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "identifying first person camera wearers in third person videos":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "what i see is what you see joint attention learning for first and third person video co analysis")
  Member "pixel level matching for video object segmentation using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "see more know more unsupervised video object segmentation with co attention siamese networks")
  Member "learning video object segmentation with visual memory":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stem seg spatio temporal embeddings for instance segmentation in videos")
  Member "online adaptation of convolutional neural networks for video object segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "segflow joint learning for video object segmentation and optical flow":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning video object segmentation from unlabeled videos")
  Member "mask r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "flownet 2 0 evolution of optical flow estimation with deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning video object segmentation from unlabeled videos")

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)