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: "discovering picturesque highlights from egocentric vacation videos"
  Abstract: We present an approach for identifying picturesque highlights from large amounts of egocentric video data. Given a set of egocentric videos captured over the course of a vacation, our method analyzes the videos and looks for images that have good picturesque and artistic properties. We introduce novel techniques to automatically determine aesthetic features such as composition, symmetry and color vibrancy in egocentric videos and rank the video frames based on their photographic qualities to generate highlights. Our approach also uses contextual information such as GPS, when available, to assess the relative importance of each geographic location where the vacation videos were shot. Furthermore, we specifically leverage the properties of egocentric videos to improve our highlight detection. We demonstrate results on a new egocentric vacation dataset which includes 26.5 hours of videos taken over a 14 day vacation that spans many famous tourist destinations and also provide results from a user-study to access our results.

Connected through hyperedge HE-1 (induced by source paper "constructing a highlight classifier with an attention based lstm neural network"):
  with members:
    - "personalized neural embeddings for collaborative filtering with text"
    - "deep bidirectional and unidirectional lstm recurrent neural network for network wide traffic speed prediction"
    - "deep learning for sentiment analysis a survey"
    - "generalizing word embeddings using bag of subwords"
    - "word mover s embedding from word2vec to document embedding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "automatic music highlight extraction using convolutional recurrent attention networks"
    - "attention is all you need"

Connected through hyperedge HE-2 (induced by source paper "learning event representations in image sequences by dynamic graph embedding"):
  with members:
    - "predicting visual context for unsupervised event segmentation in continuous photo streams"
    - "egocentric video description based on temporally linked sequences"
    - "r clustering for egocentric video segmentation"
    - "harp hierarchical representation learning for networks"
    - "representation learning on graphs methods and applications"
    - "toward storytelling from visual lifelogging an overview"
    - "sr clustering semantic regularized clustering for egocentric photo streams segmentation"
    - "geometric deep learning going beyond euclidean data"

Connected through hyperedge HE-3 (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"
    - "unsupervised learning of video representations using lstms"
    - "predicting important objects for egocentric video summarization"

Connected through hyperedge HE-4 (induced by source paper "a weighted sparse sampling and smoothing frame transition approach for semantic fast forward first person videos"):
  with members:
    - "making a long story short a multi importance fast forwarding egocentric videos with the emphasis on relevant objects"
    - "egosampling wide view hyperlapse from egocentric videos"
    - "query focused video summarization dataset evaluation and a memory network based approach"
    - "collaborative summarization of topic related videos"
    - "towards semantic fast forward and stabilized egocentric videos"
    - "fast forward video based on semantic extraction"
    - "egosampling fast forward and stereo for egocentric videos"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-5 (induced by source paper "summarizing first person videos from third persons points of views"):
  with members:
    - "a general framework for edited video and raw video summarization"
    - "video summarization with attention based encoder decoder networks"
    - "toward storytelling from visual lifelogging an overview"
    - "time contrastive networks self supervised learning from video"
    - "video summarization with long short term memory"
    - "youtube 8m a large scale video classification benchmark"
    - "video2gif automatic generation of animated gifs from video"
    - "domain separation networks"

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)