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 "on training robust pdf malware classifiers"):
  Abstract: Although state-of-the-art PDF malware classifiers can be trained with almost perfect test accuracy (99%) and extremely low false positive rate (under 0.1%), it has been shown that even a simple adversary can evade them. A practically useful malware classifier must be robust against evasion attacks. However, achieving such robustness is an extremely challenging task. #R##N#In this paper, we take the first steps towards training robust PDF malware classifiers with verifiable robustness properties. For instance, a robustness property can enforce that no matter how many pages from benign documents are inserted into a PDF malware, the classifier must still classify it as malicious. We demonstrate how the worst-case behavior of a malware classifier with respect to specific robustness properties can be formally verified. Furthermore, we find that training classifiers that satisfy formally verified robustness properties can increase the computation cost of unbounded (i.e., not bounded by the robustness properties) attackers by eliminating simple evasion attacks. #R##N#Specifically, we propose a new distance metric that operates on the PDF tree structure and specify two classes of robustness properties including subtree insertions and deletions. We utilize state-of-the-art verifiably robust training method to build robust PDF malware classifiers. Our results show that, we can achieve 99% verified robust accuracy, while maintaining 99.80% accuracy and 0.41% false positive rate. With simple robustness properties, the state-of-the-art unbounded attacker found no successful evasion on the robust classifier in 6 hours. Even for a new unbounded adaptive attacker we have designed, the number of successful evasions within a fixed time budget is cut down by 4x.

Includes members:
  Member "robust decision trees against adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robustness for non parametric classification a generic attack and defense")
  Member "enhancing gradient based attacks with symbolic intervals":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "certified defenses for adversarial patches")
  Member "scaling provable adversarial defenses":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "adversarial attack on graph structured data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "transferring robustness for graph neural network against poisoning attacks")
  Member "formal security analysis of neural networks using symbolic intervals":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "robustness verification for transformers")
  Member "on the effectiveness of interval bound propagation for training verifiably robust models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "a dual approach to scalable verification of deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "robustness verification for transformers")
  Member "cost sensitive robustness against adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "updates leak data set inference and reconstruction attacks in online learning")

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.CR(Cryptography and Security)