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 "learning assisted competitive algorithms for peak aware energy scheduling"):
  Abstract: In this paper, we study the peak-aware energy scheduling problem using the competitive framework with machine learning prediction. With the uncertainty of energy demand as the fundamental challenge, the goal is to schedule the energy output of local generation units such that the electricity bill is minimized. While this problem has been tackled using classic competitive design with worst-case guarantee, the goal of this paper is to develop learning-assisted competitive algorithms to improve the performance in a provable manner. We develop two deterministic and randomized algorithms that are provably robust against the poor performance of learning prediction, however, achieve the optimal performance as the error of prediction goes to zero. Extensive experiments using real data traces verify our theoretical observations and show 15.13% improved performance against pure online algorithms.

Includes members:
  Member "optimal algorithms for ski rental with soft machine learned predictions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online algorithms for multi shop ski rental with machine learned predictions")
  Member "online inventory management with application to energy procurement in data centers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "beyond no regret competitive control via online optimization with memory")
  Member "competitive caching with machine learned advice":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "online convex optimization for caching networks")
  Member "online convex optimization using predictions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "beyond no regret competitive control via online optimization with memory")
  Member "peak aware online economic dispatching for microgrids":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi agent meta reinforcement learning for self powered and sustainable edge computing systems")
  Member "reducing electricity demand charge for data centers with partial execution":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "online inventory management with application to energy procurement in data centers")

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.DS(Data Structures and Algorithms)