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 in auctions regret is hard envy is easy"
  Abstract: A line of recent work provides welfare guarantees of simple combinatorial auction formats, such as selling m items via simultaneous second price auctions (SiSPAs) (Christodoulou et al. 2008, Bhawalkar and Roughgarden 2011, Feldman et al. 2013). These guarantees hold even when the auctions are repeatedly executed and players use no-regret learning algorithms. Unfortunately, off-the-shelf no-regret algorithms for these auctions are computationally inefficient as the number of actions is exponential. We show that this obstacle is insurmountable: there are no polynomial-time no-regret algorithms for SiSPAs, unless RP$\supseteq$ NP, even when the bidders are unit-demand. Our lower bound raises the question of how good outcomes polynomially-bounded bidders may discover in such auctions. #R##N#To answer this question, we propose a novel concept of learning in auctions, termed "no-envy learning." This notion is founded upon Walrasian equilibrium, and we show that it is both efficiently implementable and results in approximately optimal welfare, even when the bidders have fractionally subadditive (XOS) valuations (assuming demand oracles) or coverage valuations (without demand oracles). No-envy learning outcomes are a relaxation of no-regret outcomes, which maintain their approximate welfare optimality while endowing them with computational tractability. Our results extend to other auction formats that have been studied in the literature via the smoothness paradigm. #R##N#Our results for XOS valuations are enabled by a novel Follow-The-Perturbed-Leader algorithm for settings where the number of experts is infinite, and the payoff function of the learner is non-linear. This algorithm has applications outside of auction settings, such as in security games. Our result for coverage valuations is based on a novel use of convex rounding schemes and a reduction to online convex optimization.

Connected through hyperedge HE-1 (induced by source paper "learning reserve prices in second price auctions"):
  with members:
    - "settling the sample complexity of single parameter revenue maximization"
    - "learning optimal reserve price against non myopic bidders"
    - "tight revenue gaps among simple mechanisms"
    - "tight approximation ratio of anonymous pricing"
    - "a general theory of sample complexity for multi item profit maximization"
    - "learning multi item auctions with or without samples"
    - "a sample complexity measure with applications to learning optimal auctions"
    - "simple mechanisms for subadditive buyers via duality"

Connected through hyperedge HE-2 (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-3 (induced by source paper "envy regret and social welfare loss"):
  with members:
    - "the ad types problem"
    - "online learning for measuring incentive compatibility in ad auctions"
    - "note on vcg vs price raising for matching markets"
    - "communication complexity of approximate nash equilibria"
    - "the price of anarchy in auctions"
    - "setting lower bounds on truthfulness"
    - "adam a method for stochastic optimization"
    - "gsp with general independent click through rates"

Connected through hyperedge HE-4 (induced by source paper "online learning for min max discrete problems"):
  with members:
    - "introduction to online convex optimization"
    - "learning in non convex games with an optimization oracle"
    - "efficient online linear optimization with approximation algorithms"
    - "oracle efficient online learning and auction design"
    - "efficient algorithms for adversarial contextual learning"
    - "the computational power of optimization in online learning"
    - "secretary problems with convex costs"

Connected through hyperedge HE-5 (induced by source paper "a marketplace for data an algorithmic solution"):
  with members:
    - "introduction to online convex optimization"
    - "adversarially robust generalization requires more data"
    - "statistical cost sharing"
    - "tensorflow a system for large scale machine learning"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "mllib machine learning in apache spark"
    - "bounding the estimation error of sampling based shapley value approximation with without stratifying"
    - "optimal mechanisms for selling information"

Connected through hyperedge HE-6 (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"
    - "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-7 (induced by source paper "explicit shading strategies for repeated truthful auctions"):
  with members:
    - "dynamic reserve prices for repeated auctions learning from bids"
    - "a field guide to personalized reserve prices"
    - "learning simple auctions"
    - "how to manipulate truthful prior dependent mechanisms"
    - "the sample complexity of revenue maximization"
    - "online learning in repeated auctions"
    - "learning valuation distributions from partial observation"
    - "learning theory and algorithms for revenue optimization in second price auctions with reserve"

Connected through hyperedge HE-8 (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"

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)