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: "representation quality explains adversarial attacks"
  Abstract: Neural networks have been shown vulnerable to adversarial samples, revealing that the representation learned is perhaps not as good as previously thought. Being able to evaluate to what extent are current neural networks' representations capturing the existing features would be an essential step to understand the reason for the lack of robustness. Here, we propose a way to evaluate the representation quality of neural networks using a novel type of zero-shot test, entitled Raw Zero-Shot. The main idea lies in the fact that if an algorithm learns rich features, such features should be able to describe unknown classes as well. This happens because unknown classes usually share many common features with known classes if the features learned are general enough. Two metrics are proposed to evaluate the learned features. One is based on clustering validation techniques (Davies-Bouldin Index), and the other is based on the distance to an approximated ground-truth created automatically. Experiments reveal a strong correlation between such metrics and the robustness to adversarial attacks which is further supported by a high Pearson correlation and low p-value. Interestingly, the results suggest that dynamic routing networks such as CapsNet have better representation while current deeper DNNs are trading off representational quality for accuracy.  Code available at this http URL

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)