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 "convergence of learning dynamics in stackelberg games"):
  Abstract: This paper investigates the convergence of learning dynamics in Stackelberg games. In the class of games we consider, there is a hierarchical game being played between a leader and a follower with continuous action spaces. We show that in zero-sum games, the only stable attractors of the Stackelberg gradient dynamics are Stackelberg equilibria. This insight allows us to develop a gradient-based update for the leader that converges to Stackelberg equilibria in zero-sum games and the set of stable attractors in general-sum games. We then consider a follower employing a gradient-play update rule instead of a best response strategy and propose a two-timescale algorithm with similar asymptotic convergence results. For this algorithm, we also provide finite-time high probability bounds for local convergence to a neighborhood of a stable Stackelberg equilibrium in general-sum games.

Includes members:
  Member "on gradient based learning in continuous games":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "inference based strategy alignment for general sum differential games")
  Member "what is local optimality in nonconvex nonconcave minimax optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gans may have no nash equilibria")
  Member "on gradient descent ascent for nonconvex concave minimax problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gans may have no nash equilibria")
  Member "on finding local nash equilibria and only local nash equilibria in zero sum games":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gans may have no nash equilibria")
  Member "a closer look at the optimization landscapes of generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "smooth markets a basic mechanism for organizing gradient based learners")
  Member "on the convergence of gradient based learning in continuous games":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "inference based strategy alignment for general sum differential games")
  Member "the mechanics of n player differentiable games":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "smooth markets a basic mechanism for organizing gradient based learners")
  Member "goal inference improves objective and perceived performance in human robot collaboration":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "towards better human robot collaboration with robust plan recognition and trajectory prediction")

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)