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 optimal reserve price against non myopic bidders"
  Abstract: We consider the problem of learning optimal reserve price in repeated auctions against non-myopic bidders, who may bid strategically in order to gain in future rounds even if the single-round auctions are truthful. Previous algorithms, e.g., empirical pricing, do not provide non-trivial regret rounds in this setting in general. We introduce algorithms that obtain small regret against non-myopic bidders either when the market is large, i.e., no bidder appears in a constant fraction of the rounds, or when the bidders are impatient, i.e., they discount future utility by some factor mildly bounded away from one. Our approach carefully controls what information is revealed to each bidder, and builds on techniques from differentially private online learning as well as the recent line of works on jointly differentially private algorithms.

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"
    - "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"
    - "learning simple auctions"

Connected through hyperedge HE-2 (induced by source paper "reserve pricing in repeated second price auctions with strategic bidders"):
  with members:
    - "dynamic reserve prices for repeated auctions learning from bids"
    - "optimal pricing in repeated posted price auctions"
    - "contextual search via intrinsic volumes"
    - "robust repeated auctions under heterogeneous buyer behavior"
    - "optimal dynamic auctions are virtual welfare maximizers"
    - "dynamic pricing with finitely many unknown valuations"
    - "on consistency of optimal pricing algorithms in repeated posted price auctions with strategic buyer"
    - "revenue optimization with approximate bid predictions"

Connected through hyperedge HE-3 (induced by source paper "algorithmic price discrimination"):
  with members:
    - "settling the sample complexity of single parameter revenue maximization"
    - "learning multi item auctions with or without samples"
    - "learning simple auctions"
    - "bayesian exploration incentivizing exploration in bayesian games"
    - "efficient empirical revenue maximization in single parameter auction environments"
    - "sample complexity for non truthful mechanisms"
    - "the sample complexity of revenue maximization"
    - "bayesian incentive compatible bandit exploration"

Connected through hyperedge HE-4 (induced by source paper "incentive aware contextual pricing with non parametric market noise"):
  with members:
    - "dynamic incentive aware learning robust pricing in contextual auctions"
    - "the intrinsic robustness of stochastic bandits to strategic manipulation"
    - "lp based approximation for personalized reserve prices"
    - "semi parametric dynamic contextual pricing"
    - "nonparametric learning and optimization with covariates"
    - "contextual search via intrinsic volumes"
    - "ironing in the dark"
    - "a nearly linear time ptas for explicit fractional packing and covering linear programs"

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)