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: "learning visual storylines with skipping recurrent neural networks"
  Abstract: What does a typical visit to Paris look like? Do people first take photos of the Louvre and then the Eiffel Tower? Can we visually model a temporal event like "Paris Vacation" using current frameworks? In this paper, we explore how we can automatically learn the temporal aspects, or storylines of visual concepts from web data. Previous attempts focus on consecutive image-to-image transitions and are unsuccessful at recovering the long-term underlying story. Our novel Skipping Recurrent Neural Network (S-RNN) model does not attempt to predict each and every data point in the sequence, like classic RNNs. Rather, S-RNN uses a framework that skips through the images in the photo stream to explore the space of all ordered subsets of the albums via an efficient sampling procedure. This approach reduces the negative impact of strong short-term correlations, and recovers the latent story more accurately. We show how our learned storylines can be used to analyze, predict, and summarize photo albums from Flickr. Our experimental results provide strong qualitative and quantitative evidence that S-RNN is significantly better than other candidate methods such as LSTMs on learning long-term correlations and recovering latent storylines. Moreover, we show how storylines can help machines better understand and summarize photo streams by inferring a brief personalized story of each individual album.

Connected through hyperedge HE-1 (induced by source paper "generating persuasive visual storylines for promotional videos"):
  with members:
    - "learning video story composition via recurrent neural network"
    - "nima neural image assessment"
    - "building ethics into artificial intelligence"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "building emotional machines recognizing image emotions through deep neural networks"
    - "sort story sorting jumbled images and captions into stories"
    - "will people like your image learning the aesthetic space"
    - "one weird trick for parallelizing convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "a memory network approach for story based temporal summarization of 360 deg videos"):
  with members:
    - "a deep ranking model for spatio temporal highlight detection from a 360 video"
    - "deep 360 pilot learning a deep agent for piloting through 360 deg sports video"
    - "a read write memory network for movie story understanding"
    - "hierarchically attentive rnn for album summarization and storytelling"
    - "making 360 circ video watchable in 2d learning videography for click free viewing"
    - "summary transfer exemplar based subset selection for video summarization"
    - "dynamic neural turing machine with soft and hard addressing schemes"
    - "video summarization with long short term memory"

Connected through hyperedge HE-3 (induced by source paper "ffnet video fast forwarding via reinforcement learning"):
  with members:
    - "egosampling wide view hyperlapse from egocentric videos"
    - "deep reinforcement learning based image captioning with embedding reward"
    - "collaborative summarization of topic related videos"
    - "towards semantic fast forward and stabilized egocentric videos"
    - "poseagent budget constrained 6d object pose estimation via reinforcement learning"
    - "summary transfer exemplar based subset selection for video summarization"
    - "video summarization with long short term memory"
    - "leaving some stones unturned dynamic feature prioritization for activity detection in streaming video"

Connected through hyperedge HE-4 (induced by source paper "learning video story composition via recurrent neural network"):
  with members:
    - "summary transfer exemplar based subset selection for video summarization"
    - "jointly modeling embedding and translation to bridge video and language"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "two stream convolutional networks for action recognition in videos"

Connected through hyperedge HE-5 (induced by source paper "skip rnn learning to skip state updates in recurrent neural networks"):
  with members:
    - "simple recurrent units for highly parallelizable recurrence"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "dynamic deep neural networks optimizing accuracy efficiency trade offs by selective execution"
    - "training a subsampling mechanism in expectation"
    - "a way out of the odyssey analyzing and combining recent insights for lstms"
    - "encouraging lstms to anticipate actions very early"
    - "learning to skim text"

Connected through hyperedge HE-6 (induced by source paper "visual forecasting by imitating dynamics in natural sequences"):
  with members:
    - "agent centric risk assessment accident anticipation and risky region localization"
    - "adversarial learning for neural dialogue generation"
    - "unsupervised learning for physical interaction through video prediction"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "generative adversarial imitation learning"
    - "shuffle and learn unsupervised learning using temporal order verification"
    - "learning features by watching objects move"
    - "model free imitation learning with policy optimization"

Connected through hyperedge HE-7 (induced by source paper "unsupervised visual linguistic reference resolution in instructional videos"):
  with members:
    - "joint discovery of object states and manipulation actions"
    - "modeling relationships in referential expressions with compositional modular networks"
    - "grounding of textual phrases in images by reconstruction"
    - "sort story sorting jumbled images and captions into stories"
    - "simpler context dependent logical forms via model projections"
    - "modeling context between objects for referring expression understanding"
    - "modeling context in referring expressions"
    - "generation and comprehension of unambiguous object descriptions"

Connected through hyperedge HE-8 (induced by source paper "hierarchically attentive rnn for album summarization and storytelling"):
  with members:
    - "neural summarization by extracting sentences and words"
    - "summary transfer exemplar based subset selection for video summarization"
    - "sort story sorting jumbled images and captions into stories"
    - "jointly modeling embedding and translation to bridge video and language"
    - "a neural attention model for abstractive sentence summarization"
    - "what to talk about and how selective generation using lstms with coarse to fine alignment"
    - "show attend and tell neural image caption generation with visual attention"
    - "deep residual learning for 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)