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: "econometrics for learning agents"
  Abstract: The main goal of this paper is to develop a theory of inference of player valuations from observed data in the generalized second price auction without relying on the Nash equilibrium assumption. Existing work in Economics on inferring agent values from data relies on the assumption that all participant strategies are best responses of the observed play of other players, i.e. they constitute a Nash equilibrium. In this paper, we show how to perform inference relying on a weaker assumption instead: assuming that players are using some form of no-regret learning. Learning outcomes emerged in recent years as an attractive alternative to Nash equilibrium in analyzing game outcomes, modeling players who haven't reached a stable equilibrium, but rather use algorithmic learning, aiming to learn the best way to play from previous observations. In this paper we show how to infer values of players who use algorithmic learning strategies. Such inference is an important first step before we move to testing any learning theoretic behavioral model on auction data. We apply our techniques to a dataset from Microsoft's sponsored search ad auction system.

Connected through hyperedge HE-1 (induced by source paper "robust multi agent counterfactual prediction"):
  with members:
    - "theory of minds understanding behavior in groups through inverse planning"
    - "deep counterfactual regret minimization"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "m 3 rl mind aware multi agent management reinforcement learning"
    - "reinforcement learning and inverse reinforcement learning with system 1 and system 2"
    - "optimal auctions through deep learning"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "deep reinforcement learning from self play in imperfect information games"

Connected through hyperedge HE-2 (induced by source paper "response prediction for low regret agents"):
  with members:
    - "selling to a no regret buyer"
    - "mechanism design for data science"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-3 (induced by source paper "inference from auction prices"):
  with members:
    - "a b testing of auctions"
    - "mechanism design for data science"

Connected through hyperedge HE-4 (induced by source paper "reinforcement mechanism design for e commerce"):
  with members:
    - "reinforcement mechanism design with applications to dynamic pricing in sponsored search auctions"
    - "a b testing of auctions"
    - "learning in games robustness of fast convergence"
    - "learning and efficiency in games with dynamic population"
    - "continuous control with deep reinforcement learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "no regret learning in bayesian games"
    - "memory based control with recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "selling to a no regret buyer"):
  with members:
    - "learning multi item auctions with or without samples"
    - "on the competition complexity of dynamic mechanism design"
    - "learning simple auctions"
    - "efficient empirical revenue maximization in single parameter auction environments"
    - "sequential mechanisms with ex post participation guarantees"
    - "optimal dynamic mechanisms with ex post ir via bank accounts"
    - "the sample complexity of revenue maximization"
    - "the sample complexity of auctions with side information"

Connected through hyperedge HE-6 (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"
    - "playing the wrong game smoothness bounds for congestion games with behavioral biases"

Connected through hyperedge HE-7 (induced by source paper "how bad is selfish routing in practice"):
  with members:
    - "power saving transportation mode identification for large scale applications"
    - "data driven estimation of origin destination demand and user cost functions for the optimization of transportation networks"
    - "the price of anarchy in large games"
    - "robust data driven guarantees in auctions"

Connected through hyperedge HE-8 (induced by source paper "learning and trust in auction markets"):
  with members:
    - "an experimental evaluation of regret based econometrics"
    - "a b testing of auctions"

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)