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: "future semantic segmentation of time lapsed videos with large temporal displacement"
  Abstract: An important aspect of video understanding is the ability to predict the evolution of its content in the future. This paper presents a future frame semantic segmentation technique for predicting semantic masks of the current and future frames in a time-lapsed video. We specifically focus on time-lapsed videos with large temporal displacement to highlight the model's ability to capture large motions in time. We first introduce a unique semantic segmentation prediction dataset with over 120,000 time-lapsed sky-video frames and all corresponding semantic masks captured over a span of five years in North America region. The dataset has immense practical value for cloud cover analysis, which are treated as non-rigid objects of interest. %Here the model provides both semantic segmentation of cloud region and solar irradiance emitted from a region from the sky-videos. Next, our proposed recurrent network architecture departs from existing trend of using temporal convolutional networks (TCN) (or feed-forward networks), by explicitly learning an internal representations for the evolution of video content with time. Experimental evaluation shows an improvement of mean IoU over TCNs in the segmentation task by 10.8% for 10 mins (21% over 60 mins) ahead of time predictions. Further, our model simultaneously measures both the current and future solar irradiance from the same video frames with a normalized-MAE of 10.5% over two years. These results indicate that recurrent memory networks with attention mechanism are able to capture complex advective and diffused flow characteristic of dense fluids even with sparse temporal sampling and are more suitable for future frame prediction tasks for longer duration videos.

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)