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: "when does machine learning fail generalized transferability for evasion and poisoning attacks"
  Abstract: Attacks against machine learning systems represent a growing threat as highlighted by the abundance of attacks proposed lately. However, attacks often make unrealistic assumptions about the knowledge and capabilities of adversaries. To evaluate this threat systematically, we propose the FAIL attacker model, which describes the adversary's knowledge and control along four dimensions. The FAIL model allows us to consider a wide range of weaker adversaries that have limited control and incomplete knowledge of the features, learning algorithms and training instances utilized. Within this framework, we evaluate the generalized transferability of a known evasion attack and we design StingRay, a targeted poisoning attack that is broadly applicable---it is practical against 4 machine learning applications, which use 3 different learning algorithms, and it can bypass 2 existing defenses. Our evaluation provides deeper insights into the transferability of poison and evasion samples across models and suggests promising directions for investigating defenses against this threat.

Connected through hyperedge HE-1 (induced by source paper "fine pruning defending against backdooring attacks on deep neural networks"):
  with members:
    - "stochastic activation pruning for robust adversarial defense"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"
    - "towards poisoning of deep learning algorithms with back gradient optimization"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "targeted backdoor attacks on deep learning systems using data poisoning"
    - "fine pruning joint fine tuning and compression of a convolutional network with bayesian optimization"
    - "adversarial example defenses ensembles of weak defenses are not strong"

Connected through hyperedge HE-2 (induced by source paper "security analysis of deep neural networks operating in the presence of cache side channel attacks"):
  with members:
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "i know what you see power side channel attack on convolutional neural network accelerators"
    - "understanding membership inferences on well generalized learning models"
    - "black box adversarial attacks with limited queries and information"
    - "stealing hyperparameters in machine learning"
    - "toward low flying autonomous mav trail navigation using deep neural networks for environmental awareness"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "a software approach to defeating side channels in last level caches"

Connected through hyperedge HE-3 (induced by source paper "poison frogs targeted clean label poisoning attacks on neural networks"):
  with members:
    - "certified defenses for data poisoning attacks"
    - "generative poisoning attack method against neural networks"
    - "learning under p tampering attacks"
    - "badnets identifying vulnerabilities in the machine learning model supply chain"
    - "targeted backdoor attacks on deep learning systems using data poisoning"
    - "efficient robust proper learning of log concave distributions"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

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.CR(Cryptography and Security)