human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "what i see is what you see joint attention learning for first and third person video co analysis"):
  with members:
    - "hierarchical recurrent neural network for video summarization"
    - "boosted attention leveraging human attention for image captioning"
    - "object part attention model for fine grained image classification"
    - "interaction aware spatio temporal pyramid attention networks for action classification"
    - "semantic aware attention based deep object co segmentation"
    - "factual or emotional stylized image captioning with adaptive learning and attention"
    - "cross view image synthesis using conditional gans"
    - "harmonious attention network for person re identification"

Connected through hyperedge HE-2 (induced by source paper "unsupervised traffic accident detection in first person videos"):
  with members:
    - "startnet online detection of action start in untrimmed videos"
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "real world anomaly detection in surveillance videos"
    - "egocentric vision based future vehicle localization for intelligent driving assistance systems"
    - "classifying collisions with spatio temporal action graph networks"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "temporal recurrent networks for online action detection"

Connected through hyperedge HE-3 (induced by source paper "nemo future object localization using noisy ego priors"):
  with members:
    - "drogon a causal reasoning framework for future trajectory forecast"
    - "looking to relations for future trajectory forecast"
    - "the h3d dataset for full surround 3d multi object detection and tracking in crowded urban scenes"
    - "what the constant velocity model can teach us about pedestrian motion prediction"
    - "egocentric vision based future vehicle localization for intelligent driving assistance systems"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "convolutional neural network for trajectory prediction"
    - "identifying first person camera wearers in third person videos"

Connected through hyperedge HE-4 (induced by source paper "multiple human association between top and horizontal views by matching subjects spatial distributions"):
  with members:
    - "identifying first person camera wearers in third person videos"
    - "beyond part models person retrieval with refined part pooling and a strong convolutional baseline"
    - "person re identification in the wild"
    - "egocentric meets top view"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "ego2top matching viewers in egocentric and top view videos"
    - "learning to rank in person re identification with metric ensembles"
    - "person re identification by local maximal occurrence representation and metric learning"

Connected through hyperedge HE-5 (induced by source paper "egocentric vision based future vehicle localization for intelligent driving assistance systems"):
  with members:
    - "goal oriented object importance estimation in on road driving videos"
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "temporal recurrent networks for online action detection"
    - "active object perceiver recognition guided policy learning for object searching on mobile robots"
    - "identifying first person camera wearers in third person videos"

Connected through hyperedge HE-6 (induced by source paper "egocentric meets top view"):
  with members:
    - "cross view image synthesis using conditional gans"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "ego surfing first person videos"
    - "query focused extractive video summarization"
    - "ego2top matching viewers in egocentric and top view videos"
    - "egocentric field of view localization using first person point of view devices"

Question: Please tell me which class the center node 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)