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 "bounding inefficiency of equilibria in continuous actions games using submodularity and curvature"):
  Abstract: Games with continuous strategy sets arise in several machine learning problems (e.g. adversarial learning). For such games, simple no-regret learning algorithms exist in several cases and ensure convergence to coarse correlated equilibria (CCE). The efficiency of such equilibria with respect to a social function, however, is not well understood. In this paper, we define the class of valid utility games with continuous strategies and provide efficiency bounds for their CCEs. Our bounds rely on the social function being a monotone DR-submodular function. We further refine our bounds based on the curvature of the social function. Furthermore, we extend our efficiency bounds to a class of non-submodular functions that satisfy approximate submodularity properties. Finally, we show that valid utility games with continuous strategies can be designed to maximize monotone DR-submodular functions subject to disjoint constraints with approximation guarantees. The approximation guarantees we derive are based on the efficiency of the equilibria of such games and can improve the existing ones in the literature. We illustrate and validate our results on a budget allocation game and a sensor coverage problem.

Includes members:
  Member "the mechanics of n player differentiable games":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "smooth markets a basic mechanism for organizing gradient based learners")
  Member "gradient methods for submodular maximization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "black box submodular maximization discrete and continuous settings")
  Member "guarantees for greedy maximization of non submodular functions with applications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimal approximation for unconstrained non submodular minimization")
  Member "guaranteed non convex optimization submodular maximization over continuous domains":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "online policies for efficient volunteer crowdsourcing")
  Member "submodular functions from discrete to continous domains":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "black box submodular maximization discrete and continuous settings")
  Member "submodular optimization with submodular cover and submodular knapsack constraints":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robust submodular minimization with applications to cooperative modeling")
  Member "correlated equilibria in continuous games characterization and computation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dual averaging on compactly supported distributions and application to no regret learning on a continuum")

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)