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: "the mysteries of security games equilibrium computation becomes combinatorial algorithm design"
  Abstract: The security game is a basic model for resource allocation in adversarial environments. Here there are two players, a defender and an attacker. The defender wants to allocate her limited resources to defend critical targets and the attacker seeks his most favorable target to attack. In the past decade, there has been a surge of research interest in analyzing and solving security games that are motivated by applications from various domains. This paper examines security games from a theoretical perspective and provides a unified view of various security game models. In particular, each security game can be characterized by a set system $E$ which consists of the defender's pure strategies, The defender's best response problem can be viewed as a combinatorial optimization problem over $E$. Our framework captures most of the basic security game models in the literature, including all the deployed systems, The set system $E$ arising from various domains encodes standard combinatorial problems like bipartite matching, maximum coverage, min-cost flow, packing problems, etc. Our main result shows that equilibrium computation in security games is essentially a combinatorial problem. In particular, we prove that, for any set system $E$, the following problems can be reduced to each other in polynomial time: (0) combinatorial optimization over $E$, (1) computing the minimax equilibrium for zero-sum security games over $E$, (2) computing the strong Stackelberg equilibrium for security games over $E$, (3) computing the best or worst (for the defender) Nash equilibrium for security games over $E$. Here, by "games over $E$" we mean the class of security games with arbitrary payoff structures, but a fixed set $E$ of defender pure strategies. This shows that the complexity of a security game is essentially determined by the set system $E$.

Connected through hyperedge HE-1 (induced by source paper "ai for social impact learning and planning in the data to deployment pipeline"):
  with members:
    - "group fairness in influence maximization"
    - "learning to prescribe interventions for tuberculosis patients using digital adherence data"
    - "melding the data decisions pipeline decision focused learning for combinatorial optimization"

Connected through hyperedge HE-2 (induced by source paper "decision focused learning of adversary behavior in security games"):
  with members:
    - "melding the data decisions pipeline decision focused learning for combinatorial optimization"
    - "what game are we playing end to end learning in normal and extensive form games"
    - "optnet differentiable optimization as a layer in neural networks"
    - "task based end to end model learning in stochastic optimization"
    - "predicting human behavior in unrepeated simultaneous move games"
    - "adversarial patrolling with spatially uncertain alarm signals"
    - "learning adversary behavior in security games a pac model perspective"

Connected through hyperedge HE-3 (induced by source paper "computing stackelberg equilibria of large general sum games"):
  with members:
    - "a polynomial time algorithm for spatio temporal security games"
    - "security game with non additive utilities and multiple attacker resources"
    - "oracle efficient online learning and auction design"
    - "faster and simpler algorithm for optimal strategies of blotto game"
    - "catcher evader games"
    - "bilinear games polynomial time algorithms for rank based subclasses"

Connected through hyperedge HE-4 (induced by source paper "a class of weighted tsps with applications"):
  with members:
    - "quasi regular sequences and optimal schedules for security games"
    - "linear programming based approximation algorithms for multi vehicle minimum latency problems"

Connected through hyperedge HE-5 (induced by source paper "equilibrium computation and robust optimization in zero sum games with submodular structure"):
  with members:
    - "robust optimization for non convex objectives"
    - "robust budget allocation via continuous submodular functions"
    - "algorithmic persuasion with no externalities"
    - "a reduction for optimizing lattice submodular functions with diminishing returns"
    - "correlation robust stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "a polynomial time algorithm for spatio temporal security games"):
  with members:
    - "machine learning techniques for stackelberg security games a survey"
    - "from duels to battefields computing equilibria of blotto and other games"
    - "faster and simpler algorithm for optimal strategies of blotto game"

Connected through hyperedge HE-7 (induced by source paper "security game with non additive utilities and multiple attacker resources"):
  with members:
    - "non additive security games"
    - "stackelberg vs nash in security games an extended investigation of interchangeability equivalence and uniqueness"
    - "settling the complexity of computing two player nash equilibria"

Connected through hyperedge HE-8 (induced by source paper "practical scalability for stackelberg security games"):
  with members:
    - "safe and nested subgame solving for imperfect information games"
    - "dependent randomized rounding for matroid polytopes and applications"

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)