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: "multidefender security games"
  Abstract: Stackelberg security game models and associated computational tools have seen deployment in a number of high-consequence security settings, such as LAX canine patrols and Federal Air Marshal Service. These models focus on isolated systems with only one defender, despite being part of a more complex system with multiple players. Furthermore, many real systems such as transportation networks and the power grid exhibit interdependencies between targets and, consequently, between decision makers jointly charged with protecting them. To understand such multidefender strategic interactions present in security, we investigate game theoretic models of security games with multiple defenders. Unlike most prior analysis, we focus on the situations in which each defender must protect multiple targets, so that even a single defender's best response decision is, in general, highly non-trivial. We start with an analytical investigation of multidefender security games with independent targets, offering an equilibrium and price-of-anarchy analysis of three models with increasing generality. In all models, we find that defenders have the incentive to over-protect targets, at times significantly. Additionally, in the simpler models, we find that the price of anarchy is unbounded, linearly increasing both in the number of defenders and the number of targets per defender. Considering interdependencies among targets, we develop a novel mixed-integer linear programming formulation to compute a defender's best response, and make use of this formulation in approximating Nash equilibria of the game. We apply this approach towards computational strategic analysis of several models of networks representing interdependencies, including real-world power networks. Our analysis shows how network structure and the probability of failure spread determine the propensity of defenders to over- or under-invest in security.

Connected through hyperedge HE-1 (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"
    - "stackelberg vs nash in security games an extended investigation of interchangeability equivalence and uniqueness"

Connected through hyperedge HE-2 (induced by source paper "strategic attack defense in security diffusion games"):
  with members:
    - "balancing speed and coverage by sequential seeding in complex networks"
    - "efficient computation of the shapley value for game theoretic network centrality"
    - "stochastic games for security in networks with interdependent nodes"
    - "reinforcement learning a survey"

Connected through hyperedge HE-3 (induced by source paper "defending with shared resources on a network"):
  with members:
    - "solving linear programs in the current matrix multiplication time"
    - "security game with non additive utilities and multiple attacker resources"
    - "stochastic games for security in networks with interdependent nodes"

Connected through hyperedge HE-4 (induced by source paper "be a leader or become a follower the strategy to commit to with multiple leaders extended version"):
  with members:
    - "graphical models for game theory"
    - "a general framework for computing optimal correlated equilibria in compact games"
    - "polynomial time computation of exact correlated equilibrium in compact games"

Connected through hyperedge HE-5 (induced by source paper "leadership in singleton congestion games what is hard and what is easy"):
  with members:
    - "quasi perfect stackelberg equilibrium"
    - "computing the strategy to commit to in polymatrix games extended version"
    - "computing a pessimistic leader follower equilibrium with multiple followers the mixed pure case"
    - "robust stackelberg equilibria in extensive form games and extension to limited lookahead"
    - "protection of flows under targeted attacks"
    - "generalized mirror descents in congestion games"

Connected through hyperedge HE-6 (induced by source paper "individual security and network design with malicious nodes"):
  with members:
    - "a secure approach for caching contents in wireless ad hoc networks"
    - "strategic network formation with attack and immunization"
    - "interdependent defense games modeling interdependent security under deliberate attacks"
    - "a local mean field analysis of security investments in networks"

Connected through hyperedge HE-7 (induced by source paper "adversarial regression with multiple learners"):
  with members:
    - "noncooperatively optimized tolerance decentralized strategic optimization in complex systems"
    - "robust regression and lasso"

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)