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 theory and algorithms for revenue optimization in second price auctions with reserve"
  Abstract: Second-price auctions with reserve play a critical role for modern search engine and popular online sites since the revenue of these companies often directly de- pends on the outcome of such auctions. The choice of the reserve price is the main mechanism through which the auction revenue can be influenced in these electronic markets. We cast the problem of selecting the reserve price to optimize revenue as a learning problem and present a full theoretical analysis dealing with the complex properties of the corresponding loss function. We further give novel algorithms for solving this problem and report the results of several experiments in both synthetic and real data demonstrating their effectiveness.

Connected through hyperedge HE-1 (induced by source paper "a permutation equivariant neural network architecture for auction design"):
  with members:
    - "settling the sample complexity of single parameter revenue maximization"
    - "a neural architecture for designing truthful and efficient auctions"
    - "multi agent adversarial inverse reinforcement learning"
    - "approximation schemes for a buyer with independent items via symmetries"
    - "tight revenue gaps among simple mechanisms"
    - "a duality based unified approach to bayesian mechanism design"
    - "deep sets"
    - "invariant and equivariant graph networks"

Connected through hyperedge HE-2 (induced by source paper "dynamic reserve prices for repeated auctions learning from bids"):
  with members:
    - "the sample complexity of revenue maximization"
    - "the sample complexity of auctions with side information"
    - "optimal auctions for correlated buyers with sampling"
    - "making the most of your samples"
    - "perfect bayesian equilibria in repeated sales"
    - "revenue optimization in posted price auctions with strategic buyers"
    - "learning prices for repeated auctions with strategic buyers"
    - "efficient nash computation in large population games with bounded influence"

Connected through hyperedge HE-3 (induced by source paper "dynamic incentive aware learning robust pricing in contextual auctions"):
  with members:
    - "dynamic reserve prices for repeated auctions learning from bids"
    - "contextual search via intrinsic volumes"
    - "multi armed bandit problems with strategic arms"
    - "perishability of data dynamic pricing under varying coefficient models"
    - "a field guide to personalized reserve prices"
    - "multidimensional binary search for contextual decision making"
    - "a nearly linear time ptas for explicit fractional packing and covering linear programs"
    - "learning prices for repeated auctions with strategic buyers"

Connected through hyperedge HE-4 (induced by source paper "complexity stability properties of mixed games and dynamic algorithms and learning in the sharing economy"):
  with members:
    - "multi objective pricing for shared vehicle systems"
    - "strategyproof peer selection using randomization partitioning and apportionment"
    - "approximately efficient two sided combinatorial auctions"
    - "equilibrium strategy and population size effects in lowest unique bid auctions"

Connected through hyperedge HE-5 (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-6 (induced by source paper "estimating approximate incentive compatibility"):
  with members:
    - "a duality based unified approach to bayesian mechanism design"
    - "approximate revenue maximization with multiple items"
    - "optimal auctions through deep learning"
    - "dispersion for data driven algorithm design online learning and private optimization"
    - "a general theory of sample complexity for multi item profit maximization"
    - "learning multi item auctions with or without samples"
    - "a b testing of auctions"
    - "learning simple auctions"

Connected through hyperedge HE-7 (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"
    - "learning to bid without knowing your value"
    - "optimal auctions through deep learning"
    - "a field guide to personalized reserve prices"
    - "sequential mechanisms with ex post participation guarantees"

Connected through hyperedge HE-8 (induced by source paper "algorithmic price discrimination"):
  with members:
    - "settling the sample complexity of single parameter revenue maximization"
    - "learning optimal reserve price against non myopic bidders"
    - "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"

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)