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: "efficiently learning from revealed preference"
  Abstract: In this paper, we consider the revealed preferences problem from a learning perspective. Every day, a price vector and a budget is drawn from an unknown distribution, and a rational agent buys his most preferred bundle according to some unknown utility function, subject to the given prices and budget constraint. We wish not only to find a utility function which rationalizes a finite set of observations, but to produce a hypothesis valuation function which accurately predicts the behavior of the agent in the future. We give efficient algorithms with polynomial sample-complexity for agents with linear valuation functions, as well as for agents with linearly separable, concave valuation functions with bounded second derivative.

Connected through hyperedge HE-1 (induced by source paper "grinding the space learning to classify against strategic agents"):
  with members:
    - "equal opportunity in online classification with partial feedback"
    - "distribution independent pac learning of halfspaces with massart noise"
    - "strategizing against no regret learners"
    - "introduction to multi armed bandits"
    - "nearly tight bounds for robust proper learning of halfspaces with a margin"
    - "competing prediction algorithms"
    - "strategyproof linear regression in high dimensions"
    - "strategic classification from revealed preferences"

Connected through hyperedge HE-2 (induced by source paper "the assistive multi armed bandit"):
  with members:
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "proximal policy optimization algorithms"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "high dimensional continuous control using generalized advantage estimation"
    - "learning economic parameters from revealed preferences"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "modeling human decision making in generalized gaussian multi armed bandits"

Connected through hyperedge HE-3 (induced by source paper "can a user guess what her followers want"):
  with members:
    - "steering social activity a stochastic optimal control point of view"
    - "smart broadcasting do you want to be seen"
    - "when to post on social networks"
    - "learning economic parameters from revealed preferences"
    - "how community feedback shapes user behavior"
    - "further optimal regret bounds for thompson sampling"
    - "thompson sampling for contextual bandits with linear payoffs"

Connected through hyperedge HE-4 (induced by source paper "can a user anticipate what her followers want"):
  with members:
    - "steering social activity a stochastic optimal control point of view"
    - "when to post on social networks"
    - "learning economic parameters from revealed preferences"
    - "how community feedback shapes user behavior"
    - "further optimal regret bounds for thompson sampling"
    - "thompson sampling for contextual bandits with linear payoffs"

Connected through hyperedge HE-5 (induced by source paper "incentive compatible active learning"):
  with members:
    - "strategyproof linear regression in high dimensions"
    - "learning time dependent choice"
    - "low cost learning via active data procurement"
    - "learning economic parameters from revealed preferences"
    - "prediction markets mechanism design and cooperative game theory"

Connected through hyperedge HE-6 (induced by source paper "social welfare and profit maximization from revealed preferences"):
  with members:
    - "introduction to online convex optimization"
    - "strategic classification from revealed preferences"
    - "multidimensional dynamic pricing for welfare maximization"
    - "watch and learn optimizing from revealed preferences feedback"
    - "learning economic parameters from revealed preferences"
    - "linear coupling an ultimate unification of gradient and mirror descent"
    - "fast algorithms for online stochastic convex programming"
    - "online learning and profit maximization from revealed preferences"

Connected through hyperedge HE-7 (induced by source paper "strategic classification from revealed preferences"):
  with members:
    - "kernel based methods for bandit convex optimization"
    - "multidimensional dynamic pricing for welfare maximization"
    - "watch and learn optimizing from revealed preferences feedback"
    - "learning from rational behavior predicting solutions to unknown linear programs"
    - "learning economic parameters from revealed preferences"
    - "online learning and profit maximization from revealed preferences"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-8 (induced by source paper "multidimensional dynamic pricing for welfare maximization"):
  with members:
    - "do prices coordinate markets"
    - "bandit convex optimization sqrt t regret in one dimension"
    - "watch and learn optimizing from revealed preferences feedback"
    - "learning economic parameters from revealed preferences"
    - "online learning and profit maximization from revealed preferences"
    - "combinatorial auctions via posted prices"
    - "bandits with knapsacks"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

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)