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: "source distinguishability under distortion limited attack an optimal transport perspective"
  Abstract: We analyze the distinguishability of two sources in a Neyman-Pearson set-up when an attacker is allowed to modify the output of one of the two sources subject to a distortion constraint. By casting the problem in a game-theoretic framework and by exploiting the parallelism between the attacker's goal and Optimal Transport Theory, we introduce the concept of Security Margin defined as the maximum average per-sample distortion introduced by the attacker for which the two sources can be distinguished ensuring arbitrarily small, yet positive, error exponents for type I and type II error probabilities. Several versions of the problem are considered according to the available knowledge about the sources and the type of distance used to define the distortion constraint. We compute the security margin for some classes of sources and derive a general upper bound assuming that the distortion is measured in terms of the mean square error between the original and the attacked sequence.

Connected through hyperedge HE-1 (induced by source paper "privacy against a hypothesis testing adversary"):
  with members:
    - "privacy aware smart metering progress and challenges"
    - "hypothesis testing under mutual information privacy constraints in the high privacy regime"
    - "synthesizing robust adversarial examples"
    - "hypothesis testing in the high privacy limit"
    - "adversarial machine learning at scale"
    - "smart meter privacy for multiple users in the presence of an alternative energy source"
    - "design of binary quantizers for distributed detection under secrecy constraints"
    - "on the relation between identifiability differential privacy and mutual information privacy"

Connected through hyperedge HE-2 (induced by source paper "secure detection of image manipulation by means of random feature selection"):
  with members:
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "evasion attacks against machine learning at test time"

Connected through hyperedge HE-3 (induced by source paper "detection games under fully active adversaries"):
  with members:
    - "adversarial source identification game with corrupted training"
    - "binary hypothesis testing game with training data"

Connected through hyperedge HE-4 (induced by source paper "adversarial source identification game with corrupted training"):
  with members:
    - "security evaluation of support vector machines in adversarial environments"
    - "binary hypothesis testing game with training data"

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.SY(Systems and Control)