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 "taming unbalanced training workloads in deep learning with partial collective operations"):
  Abstract: Load imbalance pervasively exists in distributed deep learning training systems, either caused by the inherent imbalance in learned tasks or by the system itself. Traditional synchronous Stochastic Gradient Descent (SGD) achieves good accuracy for a wide variety of tasks, but relies on global synchronization to accumulate the gradients at every training step. In this paper, we propose eager-SGD, which relaxes the global synchronization for decentralized accumulation. To implement eager-SGD, we propose to use two partial collectives: solo and majority. With solo allreduce, the faster processes contribute their gradients eagerly without waiting for the slower processes, whereas with majority allreduce, at least half of the participants must contribute gradients before continuing, all without using a central parameter server. We theoretically prove the convergence of the algorithms and describe the partial collectives in detail. Experimental results on load-imbalanced environments (CIFAR-10, ImageNet, and UCF101 datasets) show that eager-SGD achieves 1.27x speedup over the state-of-the-art synchronous SGD, without losing accuracy.

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 "a modular benchmarking infrastructure for high performance and reproducible deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism")
  Member "demystifying parallel and distributed deep learning an in depth concurrency analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "sparcml high performance sparse communication for machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "gossipgrad scalable deep learning using gossip communication based asynchronous gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed learning in the non convex world from batch to streaming data and beyond")
  Member "stochastic gradient push for distributed deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gradient tracking and variance reduction for decentralized optimization and machine learning")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "horovod fast and easy distributed deep learning in tensorflow":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "communication optimization strategies for distributed deep learning a survey")

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)