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 to bid without knowing your value"
  Abstract: We address online learning in complex auction settings, such as sponsored search auctions, where the value of the bidder is unknown to her, evolving in an arbitrary manner and observed only if the bidder wins an allocation. We leverage the structure of the utility of the bidder to provide algorithms with regret rates against the best fixed bid in hindsight, that are exponentially faster in convergence in terms of dependence on the action space, than what would have been derived by applying a generic bandit algorithm. Our results are enabled by analyzing a new online learning setting with outcome-based feedback, which generalizes learning with feedback graphs. We provide an online learning algorithm for this setting, of independent interest, with regret that grows only logarithmically with the number of actions and linearly only in the number of potential outcomes (the latter being very small in most auction settings).

Connected through hyperedge HE-1 (induced by source paper "discovering strategic behaviors for collaborative content production in social networks"):
  with members:
    - "inf vae a variational autoencoder framework to integrate homophily and influence in diffusion prediction"
    - "an induced multi relational framework for answer selection in community question answer platforms"
    - "dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems"
    - "session based social recommendation via dynamic graph attention networks"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "attention models in graphs a survey"
    - "deep learning on graphs a survey"

Connected through hyperedge HE-2 (induced by source paper "corrupted multidimensional binary search learning in the presence of irrational agents"):
  with members:
    - "stochastic linear optimization with adversarial corruption"
    - "corruption robust exploration in episodic reinforcement learning"
    - "better algorithms for stochastic bandits with adversarial corruptions"
    - "the intrinsic robustness of stochastic bandits to strategic manipulation"
    - "semi parametric dynamic contextual pricing"
    - "manipulating a learning defender and ways to counteract"
    - "contextual search via intrinsic volumes"
    - "stochastic bandits robust to adversarial corruptions"

Connected through hyperedge HE-3 (induced by source paper "learning to bid in revenue maximizing auctions"):
  with members:
    - "dynamic reserve prices for repeated auctions learning from bids"
    - "explicit shading strategies for repeated truthful auctions"
    - "thresholding the virtual value a simple method to increase welfare and lower reserve prices in online auction systems"
    - "thresholding at the monopoly price an agnostic way to improve bidding strategies in revenue maximizing auctions"
    - "optimal auctions through deep learning"
    - "a field guide to personalized reserve prices"
    - "sequential mechanisms with ex post participation guarantees"
    - "the sample complexity of revenue maximization"

Connected through hyperedge HE-4 (induced by source paper "online learning for measuring incentive compatibility in ad auctions"):
  with members:
    - "multiplicative pacing equilibria in auction markets"
    - "combinatorial multi armed bandit with general reward functions"
    - "online learning in repeated auctions"
    - "tight regret bounds for stochastic combinatorial semi bandits"
    - "online learning and profit maximization from revealed preferences"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "mechanism design for data science"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"

Connected through hyperedge HE-5 (induced by source paper "the intrinsic robustness of stochastic bandits to strategic manipulation"):
  with members:
    - "online learning for measuring incentive compatibility in ad auctions"
    - "bayesian exploration with heterogeneous agents"
    - "stochastic bandits robust to adversarial corruptions"
    - "multi armed bandit problems with strategic arms"
    - "bayesian incentive compatible bandit exploration"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "analysis of thompson sampling for the multi armed bandit problem"
    - "a contextual bandit approach to personalized news article recommendation"

Connected through hyperedge HE-6 (induced by source paper "contextual bandits with cross learning"):
  with members:
    - "dynamic reserve prices for repeated auctions learning from bids"
    - "incentive aware contextual pricing with non parametric market noise"
    - "learning multi item auctions with or without samples"
    - "selling to a no regret buyer"
    - "learning simple auctions"
    - "no regret learning in bayesian games"
    - "online learning in repeated auctions"
    - "online learning with feedback graphs beyond bandits"

Connected through hyperedge HE-7 (induced by source paper "dispersion for data driven algorithm design online learning and private optimization"):
  with members:
    - "a general theory of sample complexity for multi item profit maximization"
    - "revenue optimization with approximate bid predictions"
    - "a sample complexity measure with applications to learning optimal auctions"
    - "online optimization of smoothed piecewise constant functions"
    - "efficient empirical revenue maximization in single parameter auction environments"
    - "learning theoretic foundations of algorithm configuration for combinatorial partitioning problems"
    - "the sample complexity of revenue maximization"
    - "a pac approach to application specific algorithm selection"

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)