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: "visual summary of egocentric photostreams by representative keyframes"
  Abstract: Building a visual summary from an egocentric photostream captured by a lifelogging wearable camera is of high interest for different applications (e.g. memory reinforcement). In this paper, we propose a new summarization method based on keyframes selection that uses visual features extracted by means of a convolutional neural network. Our method applies an unsupervised clustering for dividing the photostreams into events, and finally extracts the most relevant keyframe for each event. We assess the results by applying a blind-taste test on a group of 20 people who assessed the quality of the summaries.

Connected through hyperedge HE-1 (induced by source paper "predicting visual context for unsupervised event segmentation in continuous photo streams"):
  with members:
    - "phd gifs personalized highlight detection for automatic gif creation"
    - "egocentric video description based on temporally linked sequences"
    - "toward storytelling from visual lifelogging an overview"
    - "sr clustering semantic regularized clustering for egocentric photo streams segmentation"
    - "hierarchically attentive rnn for album summarization and storytelling"
    - "compact cnn for indexing egocentric videos"
    - "discovering picturesque highlights from egocentric vacation videos"
    - "unsupervised learning of video representations using lstms"

Connected through hyperedge HE-2 (induced by source paper "toward storytelling from visual lifelogging an overview"):
  with members:
    - "r clustering for egocentric video segmentation"
    - "semantic summarization of egocentric photo stream events"
    - "multi face tracking by extended bag of tracklets in egocentric photo streams"
    - "f formation detection individuating free standing conversational groups in images"
    - "a survey on recent advances of computer vision algorithms for egocentric video"
    - "leveraging context to support automated food recognition in restaurants"
    - "predicting daily activities from egocentric images using deep learning"
    - "sequence to sequence video to text"

Connected through hyperedge HE-3 (induced by source paper "sr clustering semantic regularized clustering for egocentric photo streams segmentation"):
  with members:
    - "r clustering for egocentric video segmentation"
    - "toward storytelling from visual lifelogging an overview"
    - "multi face tracking by extended bag of tracklets in egocentric photo streams"
    - "with whom do i interact detecting social interactions in egocentric photo streams"
    - "compact cnn for indexing egocentric videos"
    - "predicting daily activities from egocentric images using deep learning"
    - "predicting important objects for egocentric video summarization"
    - "seeing the big picture deep embedding with contextual evidences"

Connected through hyperedge HE-4 (induced by source paper "semantic summarization of egocentric photo stream events"):
  with members:
    - "r clustering for egocentric video segmentation"
    - "toward storytelling from visual lifelogging an overview"
    - "multi face tracking by extended bag of tracklets in egocentric photo streams"
    - "ego object discovery"
    - "end to end convolutional network for saliency prediction"
    - "how transferable are features in deep neural networks"
    - "lsda large scale detection through adaptation"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-5 (induced by source paper "user driven mobile robot storyboarding learning image interest and saliency from pairwise image comparisons"):
  with members:
    - "toward storytelling from visual lifelogging an overview"
    - "protest activity detection and perceived violence estimation from social media images"
    - "deep learning the city quantifying urban perception at a global scale"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

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)