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 keyword extraction for full sentence vqa"
  Abstract: In existing studies on Visual Question Answering (VQA), which aims to train an intelligent system to be able to answer questions about images, the answers corresponding to the questions consists of short, almost single words. However, considering the natural conversation with humans, the answers would more likely to be sentences, rather than single words. In such a situation, the system needs to focus on a keyword, i.e., the most important word in the sentence, to answer the question. Therefore, we have proposed a novel keyword extraction method for VQA. Because collecting keywords and full-sentence annotations for VQA can be highly costly, we perform the keyword extraction in an unsupervised manner. Our key insight is that the full-sentence answer can be decomposed into two parts: the part contains new information for the question and the part only contains information already included in the question. Since the keyword is considered as the part which contains new information as the answer, we need to identify which words in the full-sentence answer are the part of new information and which words are not. To ensure such decomposition, we extracted two features from the full-sentence answers, and designed discriminative decoders to make each feature to include the information of the question and answers respectively. We conducted experiments on existing VQA datasets, which contains full-sentence annotations, and show that our proposed model can correctly extract the keyword without any keyword annotations.

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)