human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "sitatapatra blocking the transfer of adversarial samples"
  Abstract: Convolutional Neural Networks (CNNs) are widely used to solve classification tasks in computer vision. However, they can be tricked into misclassifying specially crafted `adversarial' samples -- and samples built to trick one model often work alarmingly well against other models trained on the same task. In this paper we introduce Sitatapatra, a system designed to block the transfer of adversarial samples. It diversifies neural networks using a key, as in cryptography, and provides a mechanism for detecting attacks. What's more, when adversarial samples are detected they can typically be traced back to the individual device that was used to develop them. The run-time overheads are minimal permitting the use of Sitatapatra on constrained systems.

Connected through hyperedge HE-1 (induced by source paper "detection and recovery of adversarial attacks with injected attractors"):
  with members:
    - "invisible backdoor attacks against deep neural networks"
    - "gotta catch em all using concealed trapdoors to detect adversarial attacks on neural networks"
    - "hidden trigger backdoor attacks"
    - "feature denoising for improving adversarial robustness"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "deepobfuscation securing the structure of convolutional neural networks via knowledge distillation"

Connected through hyperedge HE-2 (induced by source paper "towards certifiable adversarial sample detection"):
  with members:
    - "adversarial defense by restricting the hidden space of deep neural networks"
    - "certified adversarial robustness via randomized smoothing"
    - "gotta catch em all using concealed trapdoors to detect adversarial attacks on neural networks"
    - "improving adversarial robustness via promoting ensemble diversity"
    - "stateful detection of black box adversarial attacks"
    - "scaling provable adversarial defenses"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "to compress or not to compress understanding the interactions between adversarial attacks and neural network compression"

Connected through hyperedge HE-3 (induced by source paper "blackbox attacks on reinforcement learning agents using approximated temporal information"):
  with members:
    - "stateful detection of black box adversarial attacks"
    - "an algorithmic perspective on imitation learning"
    - "black box adversarial attacks with limited queries and information"
    - "behavioral cloning from observation"
    - "the taboo trap behavioural detection of adversarial samples"
    - "rainbow combining improvements in deep reinforcement learning"
    - "adversarial attacks on neural network policies"
    - "tactics of adversarial attack on deep reinforcement learning agents"

Connected through hyperedge HE-4 (induced by source paper "stateful detection of black box adversarial attacks"):
  with members:
    - "adversarial examples are a natural consequence of test error in noise"
    - "red attack resource efficient decision based attack for machine learning"
    - "on evaluating adversarial robustness"
    - "e lpips robust perceptual image similarity via random transformation ensembles"
    - "prada protecting against dnn model stealing attacks"
    - "black box adversarial attacks with limited queries and information"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"

Question: Please tell me which class the center node 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)