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: "average case performance of replicator dynamics in potential games via computing regions of attraction"
  Abstract: What does it mean to fully understand the behavior of a network of adaptive 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. We examine issues such as: #R##N#Point-wise convergence: Does the system actually equilibrate even in the presence of continuums of equilibria? #R##N#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)? #R##N#System invariants: Invariant functions remain constant along every system trajectory. This notion is orthogonal to the game theoretic concept of a potential function, which always strictly increases/decreases along system trajectories. Do dynamics in potential games exhibit invariant functions? If so, how many? How do these functions look like? #R##N#Based on these geometric characterizations, we propose a novel quantitative framework for analyzing the efficiency of potential games with many equilibria. The predictions of different equilibria are weighted by their probability to arise under evolutionary dynamics given uniformly random initial conditions. This average case analysis is shown to offer novel insights in classic game theoretic challenges, including quantifying the risk dominance in stag-hunt games and allowing for more nuanced performance analysis in networked coordination and congestion games with large gaps between price of stability and price of anarchy.

Connected through hyperedge HE-1 (induced by source paper "proportional dynamics in exchange economies"):
  with members:
    - "an alternating algorithm for finding linear arrow debreu market equilibrium"
    - "universal growth in production economies"
    - "a strongly polynomial algorithm for linear exchange markets"
    - "tracing equilibrium in dynamic markets via distributed adaptation"
    - "dynamics of distributed updating in fisher markets"
    - "selling complementary goods dynamics efficiency and revenue"
    - "best response dynamics in combinatorial auctions with item bidding"
    - "the price of anarchy in auctions"

Connected through hyperedge HE-2 (induced by source paper "alpha rank multi agent evaluation by evolution"):
  with members:
    - "emergent coordination through competition"
    - "a generalised method for empirical game theoretic analysis"
    - "re evaluating evaluation"
    - "the exact computational complexity of evolutionarily stable strategies"
    - "learning dynamics and the co evolution of competing sexual species"
    - "multiplicative weights update with constant step size in congestion games convergence limit cycles and chaos"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "deepstack expert level artificial intelligence in heads up no limit poker"

Connected through hyperedge HE-3 (induced by source paper "from darwin to poincar e and von neumann recurrence and cycles in evolutionary and algorithmic game theory"):
  with members:
    - "fast and furious learning in zero sum games vanishing regret with non vanishing step sizes"
    - "alpha rank multi agent evaluation by evolution"
    - "vortices instead of equilibria in minmax optimization chaos and butterfly effects of online learning in zero sum games"
    - "finite regret and cycles with fixed step size via alternating gradient descent ascent"
    - "multi agent learning in network zero sum games is a hamiltonian system"
    - "learning dynamics and the co evolution of competing sexual species"
    - "cycles in adversarial regularized learning"

Connected through hyperedge HE-4 (induced by source paper "universal growth in production economies"):
  with members:
    - "selling complementary goods dynamics efficiency and revenue"
    - "best response dynamics in combinatorial auctions with item bidding"
    - "nash social welfare approximation for strategic agents"
    - "the price of anarchy in auctions"
    - "learning and efficiency in games with dynamic population"
    - "learning in auctions regret is hard envy is easy"
    - "concave generalized flows with applications to market equilibria"
    - "non price equilibria in markets of discrete goods"

Connected through hyperedge HE-5 (induced by source paper "resource allocation game on social networks best response dynamics and convergence"):
  with members:
    - "fast convergence of regularized learning in games"
    - "reaching consensus via non bayesian asynchronous learning in social networks"
    - "on discrete preferences and coordination"
    - "the role of social networks in information diffusion"

Connected through hyperedge HE-6 (induced by source paper "on stochastic imitation dynamics in large scale networks"):
  with members:
    - "on imitation dynamics in potential population games"

Connected through hyperedge HE-7 (induced by source paper "prosocial learning agents solve generalized stag hunts better than selfish ones"):
  with members:
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "cooperating with machines"
    - "emergent communication in a multi modal multi step referential game"
    - "counterfactual multi agent policy gradients"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "a multi agent reinforcement learning model of common pool resource appropriation"
    - "learning with opponent learning awareness"
    - "learning cooperative visual dialog agents with deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "routing games in the wild efficiency equilibration and regret large scale field experiments in singapore"):
  with members:
    - "power saving transportation mode identification for large scale applications"
    - "learning and trust in auction markets"
    - "data driven estimation of origin destination demand and user cost functions for the optimization of transportation networks"
    - "on the price of anarchy of highly congested nonatomic network games"
    - "the price of anarchy in large games"
    - "robust data driven guarantees in auctions"
    - "learning in auctions regret is hard envy is easy"
    - "econometrics for learning agents"

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)