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 "bandit convex optimization for scalable and dynamic iot management"):
  Abstract: This paper deals with online convex optimization involving both time-varying loss functions, and time-varying constraints. The loss functions are not fully accessible to the learner, and instead only the function values (also known as bandit feedback) are revealed at queried points. The constraints are revealed after making decisions, and can be instantaneously violated, yet they must be satisfied in the long term. This setting fits nicely the emerging online network tasks such as fog computing in the Internet-of-Things, where online decisions must flexibly adapt to the changing user preferences (loss functions), and the temporally unpredictable availability of resources (constraints). Tailored for such human-in-the-loop systems where the loss functions are hard to model, a family of online bandit saddle-point (BanSaP) schemes are developed, which adaptively adjust the online operations based on (possibly multiple) bandit feedback of the loss functions, and the changing environment. Performance here is assessed by: 1)  dynamic regret  that generalizes the widely used static regret and 2)  fit  that captures the accumulated amount of constraint violations. Specifically, BanSaP is proved to simultaneously yield sublinear dynamic regret and fit, provided that the best dynamic solutions vary slowly over time. Numerical tests in fog computation offloading tasks corroborate that our proposed BanSaP approach offers competitive performance relative to existing approaches that are based on gradient feedback.

Includes members:
  Member "a survey on mobile edge computing the communication perspective":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "5g network slicing using sdn and nfv a survey of taxonomy architectures and future challenges")
  Member "an online secretary framework for fog network formation with minimal latency":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "all one needs to know about fog computing and related edge computing paradigms a complete survey")
  Member "mobile edge computing a survey on architecture and computation offloading":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fog computing systems state of the art research issues and future trends")
  Member "joint offloading and computing optimization in wireless powered mobile edge computing systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "energy efficient federated edge learning with joint communication and computation design")
  Member "online learning for wireless distributed computing":
    also connected through other hyperedges:
      (none)
  Member "online optimization competing with dynamic comparators":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "minimizing dynamic regret and adaptive regret simultaneously")
  Member "joint optimization of radio and computational resources for multicell mobile edge computing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "analysis on computation intensive status update in mobile edge computing")
  Member "an optimal algorithm for bandit and zero order convex optimization with two point feedback":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "compositional adam an adaptive compositional solver")

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.LG(Machine Learning)