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: "egocentric video description based on temporally linked sequences"
  Abstract: Egocentric vision consists in acquiring images along the day from a first person point-of-view using wearable cameras. The automatic analysis of this information allows to discover daily patterns for improving the quality of life of the user. A natural topic that arises in egocentric vision is storytelling, that is, how to understand and tell the story relying behind the pictures. In this paper, we tackle storytelling as an egocentric sequences description problem. We propose a novel methodology that exploits information from temporally neighboring events, matching precisely the nature of egocentric sequences. Furthermore, we present a new method for multimodal data fusion consisting on a multi-input attention recurrent network. We also publish the first dataset for egocentric image sequences description, consisting of 1,339 events with 3,991 descriptions, from 55 days acquired by 11 people. Furthermore, we prove that our proposal outperforms classical attentional encoder-decoder methods for video description.

Connected through hyperedge HE-1 (induced by source paper "multitask learning to improve egocentric action recognition"):
  with members:
    - "mutual context network for jointly estimating egocentric gaze and actions"
    - "saliency tubes visual explanations for spatio temporal convolutions"
    - "egocentric hand track and object based human action recognition"
    - "what would you expect anticipating egocentric actions with rolling unrolling lstms and modality attention"
    - "long term feature banks for detailed video understanding"
    - "scaling egocentric vision the epic kitchens dataset"
    - "multi fiber networks for video recognition"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-2 (induced by source paper "interactive predictive neural multimodal systems"):
  with members:
    - "direct speech to speech translation with a sequence to sequence model"
    - "online learning for effort reduction in interactive neural machine translation"
    - "nmt keras a very flexible toolkit with a focus on interactive nmt and online learning"
    - "learning transferable architectures for scalable image recognition"
    - "attention is all you need"
    - "six challenges for neural machine translation"
    - "video description using bidirectional recurrent neural networks"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-3 (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"
    - "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"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-4 (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"
    - "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"
    - "predicting important objects for egocentric video summarization"

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)