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: "learning and efficiency in games with dynamic population"
  Abstract: We study the quality of outcomes in repeated games when the population of players is dynamically changing and participants use learning algorithms to adapt to the changing environment. Game theory classically considers Nash equilibria of one-shot games, while in practice many games are players repeatedly, and in such games players often use algorithmic tools to learn to play in the given environment. Learning in repeated games has only been studied when the population playing the game is stable over time. #R##N#We analyze efficiency of repeated games in dynamically changing environments, motivated by application domains such as packet routing and Internet ad-auctions. We prove that, in many classes of games, if players choose their strategies in a way that guarantees low adaptive regret, then high social welfare is ensured, even under very frequent changes. This result extends previous work, which showed high welfare for learning outcomes in stable environments. A main technical tool for our analysis is the existence of a solution to the welfare maximization problem that is both close to optimal and relatively stable over time. Such a solution serves as a benchmark in the efficiency analysis of learning outcomes. We show that such stable and near-optimal solutions exist for many problems, even in cases when the exact optimal solution can be very unstable. We develop direct techniques to show the existence of a stable solution in some classes of games. Further, we show that a sufficient condition for the existence of stable solutions is the existence of a differentially private algorithm for the welfare maximization problem. We demonstrate our techniques by focusing on three classes of games as examples: simultaneous item auctions, bandwidth allocation mechanisms and congestion games.

Connected through hyperedge HE-1 (induced by source paper "discovering strategic behaviors for collaborative content production in social networks"):
  with members:
    - "inf vae a variational autoencoder framework to integrate homophily and influence in diffusion prediction"
    - "an induced multi relational framework for answer selection in community question answer platforms"
    - "dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems"
    - "session based social recommendation via dynamic graph attention networks"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "attention models in graphs a survey"
    - "deep learning on graphs a survey"

Connected through hyperedge HE-2 (induced by source paper "distributed no regret learning in multi agent systems"):
  with members:
    - "policy regret in repeated games"
    - "negative momentum for improved game dynamics"
    - "bandit learning in concave n person games"
    - "learning in time varying games"
    - "cycles in adversarial regularized learning"
    - "training gans with optimism"
    - "online optimization in dynamic environments improved regret rates for strongly convex problems"
    - "delay and cooperation in nonstochastic bandits"

Connected through hyperedge HE-3 (induced by source paper "memory constrained no regret learning in adversarial bandits"):
  with members:
    - "regret minimisation in multi armed bandits using bounded arm memory"
    - "learning in time varying games"
    - "stochastic multi armed bandits in constant space"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-4 (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-5 (induced by source paper "stability of open multi agent systems and applications to dynamic consensus"):
  with members:
    - "tutorial on dynamic average consensus the problem its applications and the algorithms"
    - "max consensus in open multi agent systems with gossip interactions"
    - "open multi agent systems gossiping with random arrivals and departures"

Connected through hyperedge HE-6 (induced by source paper "on preserving non discrimination when combining expert advice"):
  with members:
    - "delayed impact of fair machine learning"
    - "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem"
    - "online learning with an unknown fairness metric"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "the externalities of exploration and how data diversity helps exploitation"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "calibrated fairness in bandits"
    - "learning non discriminatory predictors"

Connected through hyperedge HE-7 (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 in auctions regret is hard envy is easy"
    - "average case performance of replicator dynamics in potential games via computing regions of attraction"
    - "concave generalized flows with applications to market equilibria"
    - "non price equilibria in markets of discrete goods"

Connected through hyperedge HE-8 (induced by source paper "learning to bid without knowing your value"):
  with members:
    - "bandits with movement costs and adaptive pricing"
    - "revenue optimization with approximate bid predictions"
    - "online learning with feedback graphs without the graphs"
    - "the sample complexity of revenue maximization"
    - "online learning in repeated auctions"
    - "online learning with feedback graphs beyond bandits"
    - "online learning and profit maximization from revealed preferences"
    - "taming the monster a fast and simple algorithm for contextual bandits"

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)