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 "qos aware machine learning based multiple resources scheduling for microservices in cloud environment"):
  Abstract: Microservices have been dominating in the modern cloud environment. To improve cost efficiency, multiple microservices are normally co-located on a server. Thus, the run-time resource scheduling becomes the pivot for QoS control. However, the scheduling exploration space enlarges rapidly with the increasing server resources - cores, cache, bandwidth, etc. - and the diversity of microservices. Consequently, the existing schedulers might not meet the rapid changes in service demands. Besides, we observe that there exist resource cliffs in the scheduling space. It not only impacts the exploration efficiency, making it difficult to converge to the optimal scheduling solution, but also results in severe QoS fluctuation. To overcome these problems, we propose a novel machine learning-based scheduling mechanism called OSML. It uses resources and runtime states as the input and employs two MLP models and a reinforcement learning model to perform scheduling space exploration. Thus, OSML can reach an optimal solution much faster than traditional approaches. More importantly, it can automatically detect the resource cliff and avoid them during exploration. To verify the effectiveness of OSML and obtain a well-generalized model, we collect a dataset containing over 2-billion samples from 11 typical microservices running on real servers over 9 months. Under the same QoS constraint, experimental results show that OSML outperforms the state-of-the-art work, and achieves around 5 times scheduling speed.

Includes members:
  Member "tensorflow a system for large scale machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "going deeper with convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discrimination aware network pruning for deep model compression")

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.DC(Distributed, Parallel, and Cluster Computing)