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: "a game theoretic analysis of adversarial classification"
  Abstract: Attack detection is usually approached as a classification problem. However, standard classification tools often perform poorly because an adaptive attacker can shape his attacks in response to the algorithm. This has led to the recent interest in developing methods for adversarial classification, but to the best of our knowledge, there have been very few prior studies that take into account the attacker's tradeoff between adapting to the classifier being used against him with his desire to maintain the efficacy of his attack. Including this effect is key to derive solutions that perform well in practice. #R##N#In this investigation we model the interaction as a game between a defender who chooses a classifier to distinguish between attacks and normal behavior based on a set of observed features and an attacker who chooses his attack features (class 1 data). Normal behavior (class 0 data) is random and exogenous. The attacker's objective balances the benefit from attacks and the cost of being detected while the defender's objective balances the benefit of a correct attack detection and the cost of false alarm. We provide an efficient algorithm to compute all Nash equilibria and a compact characterization of the possible forms of a Nash equilibrium that reveals intuitive messages on how to perform classification in the presence of an attacker. We also explore qualitatively and quantitatively the impact of the non-attacker and underlying parameters on the equilibrium strategies.

Connected through hyperedge HE-1 (induced by source paper "a survey of game theoretic approaches for adversarial machine learning in cybersecurity tasks"):
  with members:
    - "audio adversarial examples targeted attacks on speech to text"
    - "resilient linear classification an approach to deal with attacks on training data"
    - "randomized prediction games for adversarial machine learning"
    - "on the statistical detection of adversarial examples"
    - "adversarial example defenses ensembles of weak defenses are not strong"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "practical black box attacks against machine learning"
    - "generative adversarial text to image synthesis"

Connected through hyperedge HE-2 (induced by source paper "reliable smart road signs"):
  with members:
    - "semantic adversarial examples"
    - "unsupervised robotic sorting towards autonomous decision making robots"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "synthesizing robust adversarial examples"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-3 (induced by source paper "nonzero sum adversarial hypothesis testing games"):
  with members:
    - "detection games under fully active adversaries"
    - "sok security and privacy in machine learning"
    - "strategic classification from revealed preferences"
    - "adversarial hypothesis testing and a quantum stein s lemma for restricted measurements"
    - "binary hypothesis testing game with training data"
    - "stackelberg vs nash in security games an extended investigation of interchangeability equivalence and uniqueness"

Connected through hyperedge HE-4 (induced by source paper "securing infrastructure facilities when does proactive defense help"):
  with members:
    - "network flow routing under strategic link disruptions"
    - "a game theoretical approach for finding optimal strategies in an intruder classification game"
    - "stochastic games for security in networks with interdependent nodes"
    - "decision and game theory for security"

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.GT(Computer Science and Game Theory)