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: "killing four birds with one gaussian process analyzing test time attack vectors on classification"
  Abstract: The wide usage of Machine Learning (ML) leads to direct security threats, as ML algorithms are vulnerable to a plethora of attacks themselves. Different attack vectors are known, and target for example the training phase using manipulated data. Alternatively, they take place at test time and aim for miss-classification, the leakage of the training data or extraction of the model. Previous works studied different test time attacks individually. We show that using an ML model enabling formal analysis and allowing control over the decision surface curvature, interesting insights can be gained when attack vectors are not studied in isolation but in relation to each pother. We show for example how we can secure Gaussian Process Classification against empirical membership inference by properly configuring the algorithm. In this configuration, however, the model's parameters are leaked. This allows an analytic computation of the the training data, which is thus leaked, against the original intention of protecting the data. We extend our study to evasion attacks, and find that analogously, hardening the model against one attack boils down to enabling a different attacker.

Connected through hyperedge HE-1 (induced by source paper "adversarial vulnerability bounds for gaussian process classification"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "robustness guarantees for bayesian inference with gaussian processes"
    - "the limitations of model uncertainty in adversarial settings"
    - "rogue signs deceiving traffic sign recognition with malicious ads and logos"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "yes machine learning can be more secure a case study on android malware detection"
    - "improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients"
    - "provably minimally distorted 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.CR(Cryptography and Security)