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: "video question generation via cross modal self attention networks learning"
  Abstract: Video Question Answering (Video QA) is a critical and challenging task in multimedia comprehension. While deep learning based models are extremely capable of representing and understanding videos, these models heavily rely on massive data, which is expensive to label. In this paper, we introduce a novel task for automatically generating questions given a sequence of video frames and the corresponding subtitles from a clip of video to reduce the huge annotation cost. Learning to ask a question based on a video requires the model to comprehend the rich semantics in the scene and the interplay between the vision and the language. To address this, we propose a novel cross-modal self-attention (CMSA) network to aggregate the diverse features from video frames and subtitles. Excitingly, we demonstrate that our proposed model can improve the (strong) baseline from 0.0738 to 0.1374 in BLEU4 score -- more than 0.063 improvement (i.e., 85\% relatively). Most of all, We arguably pave a novel path toward solving the challenging Video QA task and provide detailed analysis which ushers the avenues for future investigations.

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)