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: "perishability of data dynamic pricing under varying coefficient models"
  Abstract: We consider a firm that sells a large number of products to its customers in an online fashion. Each product is described by a high dimensional feature vector, and the market value of a product is assumed to be linear in the values of its features. Parameters of the valuation model are unknown and can change over time. The firm sequentially observes a product's features and can use the historical sales data (binary sale/no sale feedbacks) to set the price of current product, with the objective of maximizing the collected revenue. We measure the performance of a dynamic pricing policy via regret, which is the expected revenue loss compared to a clairvoyant that knows the sequence of model parameters in advance. #R##N#We propose a pricing policy based on projected stochastic gradient descent (PSGD) and characterize its regret in terms of time $T$, features dimension $d$, and the temporal variability in the model parameters, $\delta_t$. We consider two settings. In the first one, feature vectors are chosen antagonistically by nature and we prove that the regret of PSGD pricing policy is of order $O(\sqrt{T} + \sum_{t=1}^T \sqrt{t}\delta_t)$. In the second setting (referred to as stochastic features model), the feature vectors are drawn independently from an unknown distribution. We show that in this case, the regret of PSGD pricing policy is of order $O(d^2 \log T + \sum_{t=1}^T t\delta_t/d)$.

Connected through hyperedge HE-1 (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"
    - "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"
    - "learning theory and algorithms for revenue optimization in second price auctions with reserve"

Connected through hyperedge HE-2 (induced by source paper "low rank bandit methods for high dimensional dynamic pricing"):
  with members:
    - "online learning with low rank experts"
    - "contextual bandits with latent confounders an nmf approach"
    - "kernel based methods for bandit convex optimization"
    - "low rank bandits with latent mixtures"
    - "on graduated optimization for stochastic non convex problems"
    - "the best of both worlds stochastic and adversarial bandits"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-3 (induced by source paper "contextual search via intrinsic volumes"):
  with members:
    - "online learning with an unknown fairness metric"
    - "multidimensional binary search for contextual decision making"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "living on the edge phase transitions in convex programs with random data"

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)