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: "curie a method for protecting svm classifier from poisoning attack"
  Abstract: Machine learning is used in a number of security related applications such as biometric user authentication, speaker identification etc. A type of causative integrity attack against machine learning called Poisoning attack works by injecting specially crafted data points in the training data so as to increase the false positive rate of the classifier. In the context of the biometric authentication, this means that more intruders will be classified as valid user, and in case of speaker identification system, user A will be classified user B. In this paper, we examine poisoning attack against SVM and introduce - Curie - a method to protect the SVM classifier from the poisoning attack. The basic idea of our method is to identify the poisoned data points injected by the adversary and filter them out. Our method is light weight and can be easily integrated into existing systems. Experimental results show that it works very well in filtering out the poisoned data.

Connected through hyperedge HE-1 (induced by source paper "the relationship between trust in ai and trustworthy machine learning technologies"):
  with members:
    - "decision provenance harnessing data flow for accountable systems"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "methods for interpreting and understanding deep neural networks"
    - "sok security and privacy in machine learning"
    - "yes machine learning can be more secure a case study on android malware detection"
    - "interpretable explorable approximations of black box models"
    - "a unified approach to interpreting model predictions"
    - "learning important features through propagating activation differences"

Connected through hyperedge HE-2 (induced by source paper "on defending against label flipping attacks on malware detection systems"):
  with members:
    - "can machine learning model with static features be fooled an adversarial machine learning approach"
    - "beyond supervised classification extreme minimal supervision with the graph 1 laplacian"
    - "label propagation for deep semi supervised learning"
    - "3d semi supervised learning with uncertainty aware multi view co training"
    - "is feature selection secure against training data poisoning"
    - "learning to reweight examples for robust deep learning"
    - "poison frogs targeted clean label poisoning attacks on neural networks"
    - "data poisoning attacks against online learning"

Connected through hyperedge HE-3 (induced by source paper "proving data poisoning robustness in decision trees"):
  with members:
    - "optimization and abstraction a synergistic approach for analyzing neural network robustness"
    - "certified defenses for data poisoning attacks"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "targeted backdoor attacks on deep learning systems using data poisoning"
    - "xgboost a scalable tree boosting system"
    - "towards evaluating the robustness of neural networks"
    - "intriguing properties of neural networks"
    - "poisoning attacks against support vector machines"

Connected through hyperedge HE-4 (induced by source paper "reinforcement learning for autonomous defence in software defined networking"):
  with members:
    - "semantic adversarial examples"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial attacks on neural network policies"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "certified defenses for data poisoning attacks"
    - "enhancing robustness of machine learning systems via data transformations"

Connected through hyperedge HE-5 (induced by source paper "killing four birds with one gaussian process analyzing test time attack vectors on classification"):
  with members:
    - "adversarial vulnerability for any classifier"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "the limitations of model uncertainty in adversarial settings"
    - "certified defenses against adversarial examples"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "darts deceiving autonomous cars with toxic signs"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "yes machine learning can be more secure a case study on android malware detection"

Connected through hyperedge HE-6 (induced by source paper "how wrong am i studying adversarial examples and their impact on uncertainty in gaussian process machine learning models"):
  with members:
    - "sok security and privacy in machine learning"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "statistical meta analysis of presentation attacks for secure multibiometric systems"
    - "dropout inference in bayesian neural networks with alpha divergences"
    - "deep network guided proof search"
    - "is deep learning safe for robot vision adversarial examples against the icub humanoid"
    - "simple black box adversarial perturbations for deep networks"
    - "adversarial examples attacks and defenses for deep learning"

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)