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: "good better best textual distractors generation for multi choice vqa via policy gradient"
  Abstract: Textual distractors in current multi-choice VQA datasets are not challenging enough for state-of-the-art neural models. To better assess whether well-trained VQA models are vulnerable to potential attack such as more challenging distractors, we introduce a novel task called \textit{textual Distractors Generation for VQA} (DG-VQA). The goal of DG-VQA is to generate the most confusing distractors in multi-choice VQA tasks represented as a tuple of image, question, and the correct answer. Consequently, such distractors expose the vulnerability of neural models. We show that distractor generation can be formulated as a Markov Decision Process, and present a reinforcement learning solution to unsupervised produce distractors. Our solution addresses the lack of large annotated corpus issue in classical distractor generation methods. Our proposed model receives reward signals from well-trained multi-choice VQA models and updates its parameters via policy gradient. The empirical results show that the generated textual distractors can successfully confuse several cutting-edge models with an average 20% accuracy drop from around 64%. Furthermore, we conduct extra adversarial training to improve the robustness of VQA models by incorporating the generated distractors. The experiment validates the effectiveness of adversarial training by showing a performance improvement of 27% for the multi-choice VQA task

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)