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 "interpretable deep learning under fire"):
  Abstract: Providing explanations for deep neural network (DNN) models is crucial for their usability in security-sensitive domains. A plethora of interpretation models have been proposed to help end users understand the inner workings of DNNs: how does a DNN arrive at a specific decision for a given input? The improved interpretability is believed to offer a sense of security by involving human in the decision-making process. However, due to its data-driven nature, this interpretability itself is potentially susceptible to malicious manipulations, about which little is known thus far. #R##N#In this paper, we bridge this gap by conducting the first systematic study on the security of interpretable deep learning systems (IDLSes). We demonstrate that existing IDLSes are highly vulnerable to adversarial manipulations. Specifically, we present ADV^2, a general class of attacks that generate adversarial inputs not only misleading target DNNs but also deceiving their coupled interpretation models. Through empirical evaluation against four major types of IDLSes on benchmark datasets and in security-critical applications (e.g., skin cancer diagnosis), we show that with ADV^2 the adversary is able to arbitrarily designate an input's prediction and interpretation simultaneously. Moreover, with both analytical and empirical evidence, we identify the prediction-interpretation gap as one possible cause of this vulnerability -- a DNN and its interpretation model are often only partially aligned, resulting in the possibility to exploit both models simultaneously. Finally, we explore potential countermeasures against ADV^2, including leveraging its low transferability and incorporating it in an adversarial training framework. Our findings shed light on designing and operating IDLSes in a more secure and informative fashion, leading to several promising research directions.

Includes members:
  Member "grad cam visual explanations from deep networks via gradient based localization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "towards explanation of dnn based prediction with guided feature inversion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fda feature disruptive attack")
  Member "spatially transformed adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarial ranking attack and defense")
  Member "characterizing adversarial subspaces using local intrinsic dimensionality":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "adef an iterative algorithm to construct adversarial deformations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "abba saliency regularized motion based adversarial blur attack")
  Member "is feature selection secure against training data poisoning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "certified robustness to label flipping attacks via randomized smoothing")
  Member "attacks meet interpretability attribute steered detection of adversarial samples":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "skin lesion diagnosis using ensembles unscaled multi crop evaluation and loss weighting":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "skin lesion classification using cnns with patch based attention and diagnosis guided loss weighting")

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)