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: "revenue optimization with approximate bid predictions"
  Abstract: In the context of advertising auctions, finding good reserve prices is a notoriously challenging learning problem. This is due to the heterogeneity of ad opportunity types and the non-convexity of the objective function. In this work, we show how to reduce reserve price optimization to the standard setting of prediction under squared loss, a well understood problem in the learning community. We further bound the gap between the expected bid and revenue in terms of the average loss of the predictor. This is the first result that formally relates the revenue gained to the quality of a standard machine learned model.

Connected through hyperedge HE-1 (induced by source paper "online metric algorithms with untrusted predictions"):
  with members:
    - "scheduling with predictions and the price of misprediction"
    - "near optimal bounds for online caching with machine learned advice"
    - "competitive ratio versus regret minimization achieving the best of both worlds"
    - "online computation with untrusted advice"
    - "optimal analysis of an online algorithm for the bipartite matching problem on a line"
    - "competitive caching with machine learned advice"
    - "the case for learned index structures"
    - "competitive paging algorithms"

Connected through hyperedge HE-2 (induced by source paper "online algorithms for multi shop ski rental with machine learned predictions"):
  with members:
    - "optimal algorithms for ski rental with soft machine learned predictions"
    - "learning assisted competitive algorithms for peak aware energy scheduling"
    - "a model for learned bloom filters and optimizing by sandwiching"
    - "online computation with untrusted advice"
    - "competitive caching with machine learned advice"
    - "the multi shop ski rental problem"
    - "rent lease or buy randomized algorithms for multislope ski rental"

Connected through hyperedge HE-3 (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"
    - "learning optimal reserve price against non myopic bidders"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "adversarial learning for revenue maximizing auctions"):
  with members:
    - "dynamic reserve prices for repeated auctions learning from bids"
    - "dynamic incentive aware learning robust pricing in contextual auctions"
    - "learning to bid in revenue maximizing auctions"
    - "lp based approximation for personalized reserve prices"
    - "learning to clear the market"
    - "thresholding the virtual value a simple method to increase welfare and lower reserve prices in online auction systems"
    - "a general theory of sample complexity for multi item profit maximization"
    - "a field guide to personalized reserve prices"

Connected through hyperedge HE-5 (induced by source paper "protecting the protected group circumventing harmful fairness"):
  with members:
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "competitive caching with machine learned advice"
    - "a reductions approach to fair classification"
    - "fairness behind a veil of ignorance a welfare analysis for automated decision making"
    - "algorithmic decision making and the cost of fairness"
    - "equality of opportunity in supervised learning"
    - "inherent trade offs in the fair determination of risk scores"
    - "fairness through awareness"

Connected through hyperedge HE-6 (induced by source paper "near optimal bounds for online caching with machine learned advice"):
  with members:
    - "scheduling with predictions and the price of misprediction"
    - "competitive caching with machine learned advice"
    - "the case for learned index structures"
    - "average case analysis of online topological ordering"
    - "smoothed analysis of algorithms why the simplex algorithm usually takes polynomial time"
    - "competitive paging algorithms"

Connected through hyperedge HE-7 (induced by source paper "learning to clear the market"):
  with members:
    - "a field guide to personalized reserve prices"
    - "learning simple auctions"
    - "the competition complexity of auctions a bulow klemperer result for multi dimensional bidders"
    - "the sample complexity of revenue maximization"
    - "learning theory and algorithms for revenue optimization in second price auctions with reserve"

Connected through hyperedge HE-8 (induced by source paper "optimal algorithms for ski rental with soft machine learned predictions"):
  with members:
    - "competitive caching with machine learned advice"

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.LG(Machine Learning)