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: "approximating the geometry of dynamics in potential games point wise convergence regions of attraction average case performance analysis and system invariants"
  Abstract: What does it mean to fully understand the behavior of a network of competing agents? The golden standard typically is the behavior of learning dynamics in potential games, where many evolutionary dynamics, e.g., replicator, are known to converge to sets of equilibria. Even in such classic settings many critical questions remain unanswered. Inspired by topological and geometric considerations, we devise novel yardsticks and techniques that allow for much more detailed analysis of game dynamics and network computation more generally. We address issues such as: #R##N#Point-wise convergence: Does the system actually equilibrate even in the presence of continuums of equilibria? Average case analysis: How does the system behave given a uniformly random initial condition (or more generally an initial condition chosen from a given distribution)? How does this "average case" performance compare against standard measures of efficiency such as worst case (price of anarchy) and best case analysis (price of stability)? Computing regions of attraction: Given point-wise convergence can we compute the region of asymptotic stability of each equilibrium (e.g., estimate its volume, geometry)? System invariants: A system invariant is a function defined over the system state space such that it remains constant along every system trajectory. In some sense, this is a notion that is orthogonal to the classic game theoretic concept of a potential function, which always strictly increases/decreases along system trajectories. Do dynamics in a potential game exhibit (besides the standard potential function) non-trivial invariant functions as well? If so, how many? How do these functions look like? #R##N#All the above issues prove to be tightly coupled to each other and open possibilities for a deeper understanding of many settings of networked, decentralized computation.

Connected through hyperedge HE-1 (induced by source paper "on the price of anarchy of highly congested nonatomic network games"):
  with members:
    - "the price of anarchy in large games"
    - "when does the price of anarchy tend to 1 in large walrasian auctions and fisher markets"

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)