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 "generating semantic adversarial examples with differentiable rendering"):
  Abstract: Machine learning (ML) algorithms, especially deep neural networks, have demonstrated success in several domains. However, several types of attacks have raised concerns about deploying ML in safety-critical domains, such as autonomous driving and security. An attacker perturbs a data point slightly in the concrete feature space (e.g., pixel space) and causes the ML algorithm to produce incorrect output (e.g. a perturbed stop sign is classified as a yield sign). These perturbed data points are called adversarial examples, and there are numerous algorithms in the literature for constructing adversarial examples and defending against them. In this paper we explore semantic adversarial examples (SAEs) where an attacker creates perturbations in the semantic space representing the environment that produces input for the ML model. For example, an attacker can change the background of the image to be cloudier to cause misclassification. We present an algorithm for constructing SAEs that uses recent advances in differential rendering and inverse graphics.

Includes members:
  Member "semanticadv generating adversarial examples via attribute conditional image editing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semantic adversarial perturbations using learnt representations")
  Member "should adversarial attacks use pixel p norm":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "n ml mitigating adversarial examples via ensembles of topologically manipulated classifiers")
  Member "certified adversarial robustness via randomized smoothing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "semantic adversarial attacks parametric transformations that fool deep classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a differentiable color filter for generating unrestricted adversarial images")
  Member "spatially transformed adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial ranking attack and defense")
  Member "semantic adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "breaking certified defenses semantic adversarial examples with spoofed robustness certificates")
  Member "counterexample guided data augmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "assuring the machine learning lifecycle desiderata methods and challenges")
  Member "3d aware scene manipulation via inverse graphics":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unsupervised program synthesis for images using tree structured lstm")

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)