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: "stackelberg vs nash in security games an extended investigation of interchangeability equivalence and uniqueness"
  Abstract: There has been significant recent interest in game-theoretic approaches to security, with much of the recent research focused on utilizing the leader-follower Stackelberg game model. Among the major applications are the ARMOR program deployed at LAX Airport and the IRIS program in use by the US Federal Air Marshals (FAMS). The foundational assumption for using Stackelberg games is that security forces (leaders), acting first, commit to a randomized strategy; while their adversaries (followers) choose their best response after surveillance of this randomized strategy. Yet, in many situations, a leader may face uncertainty about the follower's surveillance capability. Previous work fails to address how a leader should compute her strategy given such uncertainty.#R##N##R##N#We provide five contributions in the context of a general class of security games. First, we show that the Nash equilibria in security games are interchangeable, thus alleviating the equilibrium selection problem. Second, under a natural restriction on security games, any Stackelberg strategy is also a Nash equilibrium strategy; and furthermore, the solution is unique in a class of security games of which ARMOR is a key exemplar. Third, when faced with a follower that can attack multiple targets, many of these properties no longer hold. Fourth, we show experimentally that in most (but not all) games where the restriction does not hold, the Stackelberg strategy is still a Nash equilibrium strategy, but this is no longer true when the attacker can attack multiple targets. Finally, as a possible direction for future research, we propose an extensive-form game model that makes the defender's uncertainty about the attacker's ability to observe explicit.

Connected through hyperedge HE-1 (induced by source paper "spatial temporal moving target defense a markov stackelberg game model"):
  with members:
    - "optimal timing of moving target defense a stackelberg game model"
    - "a survey of moving target defenses for network security"

Connected through hyperedge HE-2 (induced by source paper "stackelberg equilibria for two player network routing games on parallel networks"):
  with members:
    - "on the interplay of link flooding attacks and traffic engineering"
    - "towards defeating the crossfire attack using sdn"

Connected through hyperedge HE-3 (induced by source paper "green security game with community engagement"):
  with members:
    - "camera placement based on vehicle traffic for better city security surveillance"

Connected through hyperedge HE-4 (induced by source paper "finding needles in a moving haystack prioritizing alerts with adversarial reinforcement learning"):
  with members:
    - "get your workload in order game theoretic prioritization of database auditing"
    - "rainbow combining improvements in deep reinforcement learning"
    - "a distributional perspective on reinforcement learning"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "noisy networks for exploration"
    - "asynchronous methods for deep reinforcement learning"
    - "tensorflow a system for large scale machine learning"
    - "prioritized experience replay"

Connected through hyperedge HE-5 (induced by source paper "adversarial robustness of similarity based link prediction"):
  with members:
    - "adversarial attacks on graph neural networks via meta learning"
    - "gemsec graph embedding with self clustering"
    - "hiding individuals and communities in a social network"
    - "snap a general purpose network analysis and graph mining library"
    - "semi supervised classification with graph convolutional networks"
    - "deepwalk online learning of social representations"
    - "link prediction in social networks the state of the art"

Connected through hyperedge HE-6 (induced by source paper "robust temporal difference learning for critical domains"):
  with members:
    - "a deep policy inference q network for multi agent systems"
    - "counterfactual multi agent policy gradients"
    - "multi step reinforcement learning a unifying algorithm"
    - "value decomposition networks for cooperative multi agent learning"
    - "the option critic architecture"
    - "safe and efficient off policy reinforcement learning"
    - "multidefender security games"

Connected through hyperedge HE-7 (induced by source paper "autonomous intelligent cyber defense agent aica reference architecture release 2 0"):
  with members:
    - "initial reference architecture of an intelligent autonomous agent for cyber defense"
    - "toward intelligent autonomous agents for cyber defense report of the 2017 workshop by the north atlantic treaty organization nato research group ist 152 rtg"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "pomdps make better hackers accounting for uncertainty in penetration testing"
    - "playing atari with deep reinforcement learning"
    - "an algorithm to find optimal attack paths in nondeterministic scenarios"

Connected through hyperedge HE-8 (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"
    - "a game theoretic analysis of adversarial classification"
    - "adversarial hypothesis testing and a quantum stein s lemma for restricted measurements"
    - "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.GT(Computer Science and Game Theory)