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 economic parameters from revealed preferences"
  Abstract: A recent line of work, starting with Beigman and Vohra (2006) and Zadimoghaddam and Roth (2012), has addressed the problem of {\em learning} a utility function from revealed preference data. The goal here is to make use of past data describing the purchases of a utility maximizing agent when faced with certain prices and budget constraints in order to produce a hypothesis function that can accurately forecast the {\em future} behavior of the agent. #R##N#In this work we advance this line of work by providing sample complexity guarantees and efficient algorithms for a number of important classes. By drawing a connection to recent advances in multi-class learning, we provide a computationally efficient algorithm with tight sample complexity guarantees ($\Theta(d/\epsilon)$ for the case of $d$ goods) for learning linear utility functions under a linear price model. This solves an open question in Zadimoghaddam and Roth (2012). Our technique yields numerous generalizations including the ability to learn other well-studied classes of utility functions, to deal with a misspecified model, and with non-linear prices.

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"
    - "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"
    - "efficient bayes adaptive reinforcement learning using sample based search"

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"
    - "how community feedback shapes user behavior"
    - "further optimal regret bounds for thompson sampling"
    - "efficiently learning from revealed preference"
    - "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"
    - "how community feedback shapes user behavior"
    - "further optimal regret bounds for thompson sampling"
    - "efficiently learning from revealed preference"
    - "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"
    - "efficiently learning from revealed preference"
    - "prediction markets mechanism design and cooperative game theory"

Connected through hyperedge HE-6 (induced by source paper "a general theory of sample complexity for multi item profit maximization"):
  with members:
    - "a duality based unified approach to bayesian mechanism design"
    - "approximate revenue maximization with multiple items"
    - "submultiplicative glivenko cantelli and uniform convergence of revenues"
    - "learning multi item auctions with or without samples"
    - "revenue optimization with approximate bid predictions"
    - "a sample complexity measure with applications to learning optimal auctions"
    - "learning simple auctions"
    - "do prices coordinate markets"

Connected through hyperedge HE-7 (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"
    - "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"
    - "learning what s going on reconstructing preferences and priorities from opaque transactions"

Connected through hyperedge HE-8 (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"
    - "online learning and profit maximization from revealed preferences"
    - "efficiently learning from revealed preference"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

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)