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: "ego surfing first person videos"
  Abstract: We envision a future time when wearable cameras are worn by the masses, recording first-person point-of-view (POV) videos of everyday life. While these cameras can enable new assistive technologies and novel research challenges, they also raise serious privacy concerns. For example, first-person videos passively recorded by wearable cameras will necessarily include anyone who comes into the view of a camera -- with or without consent. Motivated by these benefits and risks, we developed a self-search technique tailored to first-person videos. The key observation of our work is that the egocentric head motion of a target person (i.e., the self) is observed both in the POV video of the target and observer. The motion correlation between the target person's video and the observer's video can then be used to identify instances of the self uniquely. We incorporate this feature into the proposed approach that computes the motion correlation over densely-sampled trajectories to search for a target in observer videos. Our approach significantly improves self-search performance over several well-known face detectors and recognizers. Furthermore, we show how our approach can enable several practical applications such as privacy filtering, target video retrieval, and social group clustering.

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 "joint person segmentation and identification in synchronized first and third person videos"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "identifying first person camera wearers in third person videos"
    - "pixel level matching for video object segmentation using convolutional neural networks"
    - "learning video object segmentation with visual memory"
    - "online adaptation of convolutional neural networks for video object segmentation"
    - "segflow joint learning for video object segmentation and optical flow"
    - "mask r cnn"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"

Connected through hyperedge HE-3 (induced by source paper "multi stream dynamic video summarization"):
  with members:
    - "hierarchical recurrent neural network for video summarization"
    - "video summarization via actionness ranking"
    - "how local is the local diversity reinforcing sequential determinantal point processes with dynamic ground sets for supervised video summarization"
    - "improving sequential determinantal point processes for supervised video summarization"
    - "diversity aware multi video summarization"
    - "identifying first person camera wearers in third person videos"
    - "video summarization with attention based encoder decoder networks"
    - "query focused video summarization dataset evaluation and a memory network based approach"

Connected through hyperedge HE-4 (induced by source paper "actor and observer joint modeling of first and third person videos"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "multimodal machine learning a survey and taxonomy"
    - "identifying first person camera wearers in third person videos"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "learning features by watching objects move"
    - "first person activity forecasting with online inverse reinforcement learning"
    - "the open world of micro videos"
    - "learning action maps of large environments via first person vision"

Connected through hyperedge HE-5 (induced by source paper "ego surfing person localization in first person videos using ego motion signatures"):
  with members:
    - "ego2top matching viewers in egocentric and top view videos"
    - "an egocentric look at video photographer identity"

Connected through hyperedge HE-6 (induced by source paper "identifying first person camera wearers in third person videos"):
  with members:
    - "facenet a unified embedding for face recognition and clustering"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "caffe convolutional architecture for fast feature embedding"
    - "two stream convolutional networks for action recognition in videos"

Connected through hyperedge HE-7 (induced by source paper "compact cnn for indexing egocentric videos"):
  with members:
    - "egosampling fast forward and stereo for egocentric videos"
    - "pooled motion features for first person videos"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "caffe convolutional architecture for fast feature embedding"
    - "modeep a deep learning framework using motion features for human pose estimation"
    - "finding action tubes"
    - "egocentric video biometrics"

Connected through hyperedge HE-8 (induced by source paper "egocentric meets top view"):
  with members:
    - "cross view image synthesis using conditional gans"
    - "joint person segmentation and identification in synchronized first and third person videos"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "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)