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: "an egocentric look at video photographer identity"
  Abstract: Egocentric cameras are being worn by an increasing number of users, among them many security forces worldwide. GoPro cameras already penetrated the mass market, reporting substantial increase in sales every year. As head-worn cameras do not capture the photographer, it may seem that the anonymity of the photographer is preserved even when the video is publicly distributed. #R##N#We show that camera motion, as can be computed from the egocentric video, provides unique identity information. The photographer can be reliably recognized from a few seconds of video captured when walking. The proposed method achieves more than 90% recognition accuracy in cases where the random success rate is only 3%. #R##N#Applications can include theft prevention by locking the camera when not worn by its rightful owner. Searching video sharing services (e.g. YouTube) for egocentric videos shot by a specific photographer may also become possible. An important message in this paper is that photographers should be aware that sharing egocentric video will compromise their anonymity, even when their face is not visible.

Connected through hyperedge HE-1 (induced by source paper "ego topo environment affordances from egocentric video"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "scene memory transformer for embodied agents in long horizon tasks"
    - "videograph recognizing minutes long human activities in videos"
    - "anticipation and next action forecasting in video an end to end model with memory"
    - "generative hybrid representations for activity forecasting with no regret learning"
    - "what would you expect anticipating egocentric actions with rolling unrolling lstms and modality attention"
    - "long term feature banks for detailed video understanding"
    - "binge watching scaling affordance learning from sitcoms"

Connected through hyperedge HE-2 (induced by source paper "a report on personally identifiable sensor data from smartphone devices"):
  with members:
    - "predicting floor level for 911 calls with neural networks and smartphone sensor data"
    - "mobile phone identification through the built in magnetometers"
    - "planet photo geolocation with convolutional neural networks"
    - "seeing behind the camera identifying the authorship of a photograph"
    - "hmog new behavioral biometric features for continuous authentication of smartphone users"
    - "data driven authentication on the effectiveness of user behaviour modelling with mobile device sensors"
    - "pin skimming exploiting the ambient light sensor in mobile devices"
    - "mobile device identification via sensor fingerprinting"

Connected through hyperedge HE-3 (induced by source paper "ego surfing person localization in first person videos using ego motion signatures"):
  with members:
    - "ego surfing first person videos"
    - "ego2top matching viewers in egocentric and top view videos"

Connected through hyperedge HE-4 (induced by source paper "egosampling wide view hyperlapse from egocentric videos"):
  with members:
    - "compact cnn for indexing egocentric videos"
    - "pooled motion features for first person videos"

Connected through hyperedge HE-5 (induced by source paper "analyzing first person stories based on socializing eating and sedentary patterns"):
  with members:
    - "recognizing activities of daily living from egocentric images"
    - "sentiment recognition in egocentric photostreams"
    - "toward storytelling from visual lifelogging an overview"
    - "sr clustering semantic regularized clustering for egocentric photo streams segmentation"
    - "with whom do i interact detecting social interactions in egocentric photo streams"
    - "simultaneous food localization and recognition"
    - "tensorflow a system for large scale machine learning"
    - "the fast convergence of incremental pca"

Connected through hyperedge HE-6 (induced by source paper "future person localization in first person videos"):
  with members:
    - "forecasting hand and object locations in future frames"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "unsupervised learning of depth and ego motion from video"
    - "next active object prediction from egocentric videos"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "deep learning driven visual path prediction from a single image"
    - "first person activity forecasting with online inverse reinforcement learning"
    - "forecasting interactive dynamics of pedestrians with fictitious play"

Connected through hyperedge HE-7 (induced by source paper "egocentric hand detection via dynamic region growing"):
  with members:
    - "driver hand localization and grasp analysis a vision based real time approach"
    - "joint hand detection and rotation estimation by using cnn"
    - "walk and learn facial attribute representation learning from egocentric video and contextual data"
    - "a survey on recent advances of computer vision algorithms for egocentric video"
    - "the evolution of first person vision methods a survey"

Connected through hyperedge HE-8 (induced by source paper "egocentric height estimation"):
  with members:
    - "multi face tracking by extended bag of tracklets in egocentric photo streams"
    - "walk and learn facial attribute representation learning from egocentric video and contextual data"
    - "compact cnn for indexing egocentric videos"
    - "predicting important objects for egocentric video summarization"
    - "the evolution of first person vision methods a survey"
    - "two stream convolutional networks for action recognition in videos"

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)