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: "unsupervised extraction of video highlights via robust recurrent auto encoders"
  Abstract: With the growing popularity of short-form video sharing platforms such as \em{Instagram} and \em{Vine}, there has been an increasing need for techniques that automatically extract highlights from video. Whereas prior works have approached this problem with heuristic rules or supervised learning, we present an unsupervised learning approach that takes advantage of the abundance of user-edited videos on social media websites such as YouTube. Based on the idea that the most significant sub-events within a video class are commonly present among edited videos while less interesting ones appear less frequently, we identify the significant sub-events via a robust recurrent auto-encoder trained on a collection of user-edited videos queried for each particular class of interest. The auto-encoder is trained using a proposed shrinking exponential loss function that makes it robust to noise in the web-crawled training data, and is configured with bidirectional long short term memory (LSTM)~\cite{LSTM:97} cells to better model the temporal structure of highlight segments. Different from supervised techniques, our method can infer highlights using only a set of downloaded edited videos, without also needing their pre-edited counterparts which are rarely available online. Extensive experiments indicate the promise of our proposed solution in this challenging unsupervised settin

Connected through hyperedge HE-1 (induced by source paper "summarynet a multi stage deep learning model for automatic video summarisation"):
  with members:
    - "cycle sum cycle consistent adversarial lstm networks for unsupervised video summarization"
    - "video summarization via actionness ranking"
    - "discriminative feature learning for unsupervised video summarization"
    - "video summarization using fully convolutional sequence networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward"
    - "aenet learning deep audio features for video analysis"
    - "video summarization with long short term memory"

Connected through hyperedge HE-2 (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-3 (induced by source paper "video skimming taxonomy and comprehensive survey"):
  with members:
    - "hierarchical recurrent neural network for video summarization"
    - "a general framework for edited video and raw video summarization"
    - "phd gifs personalized highlight detection for automatic gif creation"
    - "discovery of shared semantic spaces for multiscene video query and summarization"
    - "summarizing first person videos from third persons points of views"
    - "video summarization with attention based encoder decoder networks"
    - "query focused video summarization dataset evaluation and a memory network based approach"
    - "collaborative summarization of topic related videos"

Connected through hyperedge HE-4 (induced by source paper "motion aware feature for improved video anomaly detection"):
  with members:
    - "graphical contrastive losses for scene graph parsing"
    - "real world anomaly detection in surveillance videos"
    - "towards universal representation for unseen action recognition"
    - "deepvs a deep learning based video saliency prediction approach"
    - "large scale visual relationship understanding"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"

Connected through hyperedge HE-5 (induced by source paper "goal driven sequential data abstraction"):
  with members:
    - "learning deep sketch abstraction"
    - "game of sketches deep recurrent models of pictionary style word guessing"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "dtr gan dilated temporal relational adversarial network for video summarization"
    - "video summarisation by classification with deep reinforcement learning"
    - "discovering visual concept structure with sparse and incomplete tags"
    - "sequential dual deep learning with shape and texture features for sketch recognition"
    - "neural extractive summarization with side information"

Connected through hyperedge HE-6 (induced by source paper "trunet short videos generation from long videos via story preserving truncation"):
  with members:
    - "bsn boundary sensitive network for temporal action proposal generation"
    - "single shot temporal action detection"
    - "temporal action detection with structured segment networks"
    - "temporal context network for activity localization in videos"
    - "temporal action localization by structured maximal sums"
    - "turn tap temporal unit regression network for temporal action proposals"
    - "a pursuit of temporal accuracy in general activity detection"
    - "temporal action localization in untrimmed videos via multi stage cnns"

Connected through hyperedge HE-7 (induced by source paper "less is more learning highlight detection from video duration"):
  with members:
    - "attention based deep multiple instance learning"
    - "exploring the limits of weakly supervised pretraining"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "collaborative summarization of topic related videos"
    - "learning from noisy labels with distillation"
    - "video summarization with long short term memory"
    - "youtube 8m a large scale video classification benchmark"

Connected through hyperedge HE-8 (induced by source paper "hierarchical recurrent neural network for video summarization"):
  with members:
    - "summary transfer exemplar based subset selection for video summarization"
    - "video summarization with long short term memory"
    - "title generation for user generated videos"
    - "beyond short snippets deep networks for video classification"
    - "hierarchical recurrent neural encoder for video representation with application to captioning"
    - "sequence to sequence video to text"
    - "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)