human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "social welfare and profit maximization from revealed preferences"):
  Abstract: Consider the seller's problem of finding "optimal" prices for her (divisible) goods when faced with a set of consumers, given that she can only observe their purchased bundles at set prices, i.e., access to only revealed preferences (demand oracle). We study both social welfare and profit maximization under this setting, assuming concave valuation function of consumers and convex production cost function of the seller, standard assumptions in economics. #R##N#Recent series of works (Roth et al., 2016, 2017) studied this problem for various special cases of valuation and cost functions, while making no assumption on the demand oracle. In this work, for social-welfare maximization, we obtain a natural interpretation of the revealed preference feedback in the dual optimization problem, and thereby obtain a simple gradient based algorithm. This simplifies and improves on (Roth et al., 2016, 2017) at least by a quadratic factor in terms of query complexity. #R##N#Second, we study social-welfare maximization under the online setting, where consumers arrive one-by-one in a {\em random order} a.k.a. secretary model. For the case where consumer valuation can be arbitrary continuous function, we design a price posting mechanism that achieves average expected social welfare up to an additive factor of $\frac{1}{\sqrt{m}}$ of maximum social welfare, where $m$ is the number of consumers. #R##N#Third, for the profit maximization, we obtain the first FPTAS when valuation and cost functions are separable, with a matching lower bound. For the non-separable case we show that no PTAS exists, even when only cost function is non-separable.

Includes members:
  Member "introduction to online convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "strategic classification from revealed preferences":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "decisions counterfactual explanations and strategic behavior")
  Member "multidimensional dynamic pricing for welfare maximization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "grinding the space learning to classify against strategic agents")
  Member "watch and learn optimizing from revealed preferences feedback":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "grinding the space learning to classify against strategic agents")
  Member "learning economic parameters from revealed preferences":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "grinding the space learning to classify against strategic agents")
  Member "linear coupling an ultimate unification of gradient and mirror descent":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "faster on device training using new federated momentum algorithm")
  Member "fast algorithms for online stochastic convex programming":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "simple and fast algorithm for binary integer and online linear programming")
  Member "online learning and profit maximization from revealed preferences":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "grinding the space learning to classify against strategic agents")

Question: Please tell me which class the source paper 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)