human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "predicting visual context for unsupervised event segmentation in continuous photo streams"):
  Abstract: Segmenting video content into events provides semantic structures for indexing, retrieval, and summarization. Since motion cues are not available in continuous photo-streams, and annotations in lifelogging are scarce and costly, the frames are usually clustered into events by comparing the visual features between them in an unsupervised way. However, such methodologies are ineffective to deal with heterogeneous events, e.g. taking a walk, and temporary changes in the sight direction, e.g. at a meeting. To address these limitations, we propose Contextual Event Segmentation (CES), a novel segmentation paradigm that uses an LSTM-based generative network to model the photo-stream sequences, predict their visual context, and track their evolution. CES decides whether a frame is an event boundary by comparing the visual context generated from the frames in the past, to the visual context predicted from the future. We implemented CES on a new and massive lifelogging dataset consisting of more than 1.5 million images spanning over 1,723 days. Experiments on the popular EDUB-Seg dataset show that our model outperforms the state-of-the-art by over 16% in f-measure. Furthermore, CES' performance is only 3 points below that of human annotators.

Includes members:
  Member "phd gifs personalized highlight detection for automatic gif creation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "video skimming taxonomy and comprehensive survey")
  Member "egocentric video description based on temporally linked sequences":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multitask learning to improve egocentric action recognition")
  Member "toward storytelling from visual lifelogging an overview":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "analysis of the hands in egocentric vision a survey")
  Member "sr clustering semantic regularized clustering for egocentric photo streams segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning event representations in image sequences by dynamic graph embedding")
  Member "hierarchically attentive rnn for album summarization and storytelling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hide and tell learning to bridge photo streams for visual storytelling")
  Member "compact cnn for indexing egocentric videos":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "h o unified egocentric recognition of 3d hand object poses and interactions")
  Member "discovering picturesque highlights from egocentric vacation videos":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "constructing a highlight classifier with an attention based lstm neural network")
  Member "unsupervised learning of video representations using lstms":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "transformation based adversarial video prediction on large scale data")

Question: Please tell me which class the source paper 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)