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 "are you tampering with my data"):
  Abstract: We propose a novel approach towards adversarial attacks on neural networks (NN), focusing on tampering the data used for training instead of generating attacks on trained models. Our network-agnostic method creates a backdoor during training which can be exploited at test time to force a neural network to exhibit abnormal behaviour. We demonstrate on two widely used datasets (CIFAR-10 and SVHN) that a universal modification of just one pixel per image for all the images of a class in the training set is enough to corrupt the training procedure of several state-of-the-art deep neural networks causing the networks to misclassify any images to which the modification is applied. Our aim is to bring to the attention of the machine learning community, the possibility that even learning-based methods that are personally trained on public datasets can be subject to attacks by a skillful adversary.

Includes members:
  Member "one pixel attack for fooling deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial ranking attack and defense")
  Member "laplacian networks bounding indicator function smoothness for neural networks robustness":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep geometric knowledge distillation with graphs")
  Member "deepdiva a highly functional python framework for reproducible experiments":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a comprehensive study of imagenet pre training for historical document image analysis")
  Member "is feature selection secure against training data poisoning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "certified robustness to label flipping attacks via randomized smoothing")
  Member "is data clustering in adversarial settings secure":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial learning in statistical classification a comprehensive review of defenses against attacks")
  Member "potrojan powerful neural level trojan designs in deep learning models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "backdoor attacks against transfer learning with pre trained deep learning models")
  Member "why do deep convolutional networks generalize so poorly to small image transformations":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "affinity and diversity quantifying mechanisms of data augmentation")
  Member "peernets exploiting peer wisdom against adversarial attacks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "certified robustness for top k predictions against adversarial perturbations via randomized smoothing")

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)