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: "semantic robustness of models of source code"
  Abstract: Deep neural networks are vulnerable to adversarial examples - small input perturbations that result in incorrect predictions. We study this problem in the context of models of source code, where we want the network to be robust to source-code modifications that preserve code functionality. We define a natural notion of robustness, $k$-transformation robustness, in which an adversary performs up to $k$ semantics-preserving transformations to an input program. We show how to train robust models using an adversarial training objective inspired by that of Madry et al. (2018) for continuous domains. #R##N#We implement an extensible framework for adversarial training over source code, and conduct a thorough evaluation on a number of datasets and two different architectures. Our results show (1) the increase in robustness following adversarial training, (2) the ability of training on weak adversaries to provide robustness to attacks by stronger adversaries, and (3) the shift in attribution focus of adversarially trained models towards semantic vs. syntactic features.

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.LG(Machine Learning)