human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "efficient formal safety analysis of neural networks"):
  Abstract: Neural networks are increasingly deployed in real-world safety-critical domains such as autonomous driving, aircraft collision avoidance, and malware detection. However, these networks have been shown to often mispredict on inputs with minor adversarial or even accidental perturbations. Consequences of such errors can be disastrous and even potentially fatal as shown by the recent Tesla autopilot crash. Thus, there is an urgent need for formal analysis systems that can rigorously check neural networks for violations of different safety properties such as robustness against adversarial perturbations within a certain $L$-norm of a given image. An effective safety analysis system for a neural network must be able to either ensure that a safety property is satisfied by the network or find a counterexample, i.e., an input for which the network will violate the property. Unfortunately, most existing techniques for performing such analysis struggle to scale beyond very small networks and the ones that can scale to larger networks suffer from high false positives and cannot produce concrete counterexamples in case of a property violation. In this paper, we present a new efficient approach for rigorously checking different safety properties of neural networks that significantly outperforms existing approaches by multiple orders of magnitude. Our approach can check different safety properties and find concrete counterexamples for networks that are 10$\times$ larger than the ones supported by existing analysis techniques. We believe that our approach to estimating tight output bounds of a network for a given input range can also help improve the explainability of neural networks and guide the training process of more robust neural networks.

Includes members:
  Member "scaling provable adversarial defenses":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "formal security analysis of neural networks using symbolic intervals":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "robustness verification for transformers")
  Member "training verified learners with learned verifiers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "black box smoothing a provable defense for pretrained classifiers")
  Member "deep neural networks as 0 1 mixed integer linear programs a feasibility study":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "randomized smoothing of all shapes and sizes")
  Member "provable defenses against adversarial examples via the convex outer adversarial polytope":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "towards proving the adversarial robustness of deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robustness for non parametric classification a generic attack and defense")
  Member "towards practical verification of machine learning the case of computer vision systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "certification of semantic perturbations via randomized smoothing")
  Member "deepxplore automated whitebox testing of deep learning systems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "importance driven deep learning system testing")

Question: Please tell me which class the source paper 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)