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 "elastic deep learning in multi tenant gpu cluster"):
  Abstract: Multi-tenant GPU clusters are common nowadays due to the huge success of deep learning and training jobs are usually conducted with multiple distributed GPUs. These GPU clusters are managed with various goals including short JCT, high resource utilization and quick response to small jobs. #R##N#In this paper, we show that elasticity, which is the ability to adjust the parallelism (number of GPUs) of a job with low overhead, helps to achieve the goals of GPU cluster management. With elasticity, we can adjust the trade-off between throughput and efficiency, adapt to the cluster load variations, utilize transient idle resource and etc. Motivated by the benefits of elasticity, we designed Amoeba, which requires minimum change to user code and provides a simple API for the scheduler to control the parallelism of jobs. Amoeba is general in that it delegates single machine execution to existing deep learning frameworks and uses light-weight control layer for coordination and management. As it is crucial to reduce the overhead of parallelism adjustment, Amoeba adopts key designs including automatic job management, background scaling and dynamic data pipeline. #R##N#Experimental results show that Amoeba introduces negligible overhead to normal training without parallelism adjustment and pays significantly lower cost (around 95%) for scaling comparing with naive stop-resume. Moreover, we also show that state-of-the-art GPU cluster scheduler can leverage elasticity with simple modifications and reduce the average JCT by as much as 29% over the case without elasticity.

Includes members:
  Member "priority based parameter propagation for distributed dnn training":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "dl2 a deep learning driven scheduler for deep learning clusters":
    also connected through other hyperedges:
      (none)
  Member "analysis of large scale multi tenant gpu clusters for dnn training workloads":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "understanding and optimizing packed neural network training for hyper parameter tuning")
  Member "horovod fast and easy distributed deep learning in tensorflow":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "poseidon an efficient communication architecture for distributed deep learning on gpu clusters":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "accurate large minibatch sgd training imagenet in 1 hour":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning representations by predicting bags of visual words")
  Member "tensorflow a system for large scale machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "deep residual learning for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)