human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "decision focused learning of adversary behavior in security games"):
  Abstract: Stackelberg security games are a critical tool for maximizing the utility of limited defense resources to protect important targets from an intelligent adversary. Motivated by green security, where the defender may only observe an adversary's response to defense on a limited set of targets, we study the problem of defending against the same adversary on a larger set of targets from the same distribution. We give a theoretical justification for why standard two-stage learning approaches, where a model of the adversary is trained for predictive accuracy and then optimized against, may fail to maximize the defender's expected utility in this setting. We develop a decision-focused learning approach, where the adversary behavior model is optimized for decision quality, and show empirically that it achieves higher defender expected utility than the two-stage approach when there is limited training data and a large number of target features.

Includes members:
  Member "melding the data decisions pipeline decision focused learning for combinatorial optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "decision trees for decision making under the predict then optimize framework")
  Member "what game are we playing end to end learning in normal and extensive form games":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "differentiable convex optimization layers")
  Member "optnet differentiable optimization as a layer in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lossless compression of deep neural networks")
  Member "task based end to end model learning in stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "lossless compression of deep neural networks")
  Member "predicting human behavior in unrepeated simultaneous move games":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the hanabi challenge a new frontier for ai research")
  Member "the mysteries of security games equilibrium computation becomes combinatorial algorithm design":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ai for social impact learning and planning in the data to deployment pipeline")
  Member "adversarial patrolling with spatially uncertain alarm signals":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "computing the strategy to commit to in polymatrix games extended version")
  Member "learning adversary behavior in security games a pac model perspective":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sok security and privacy in machine learning")

Question: Please tell me which class the source paper 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)