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 "accelerated training for cnn distributed deep learning through automatic resource aware layer placement"):
  Abstract: The Convolutional Neural Network (CNN) model, often used for image classification, requires significant training time to obtain high accuracy. To this end, distributed training is performed with the parameter server (PS) architecture using multiple servers. Unfortunately, scalability has been found to be poor in existing architectures. We find that the PS network is the bottleneck as it communicates a large number of gradients and parameters with the many workers. This is because synchronization with the many workers has to occur at every step of training. Depending on the model, communication can be in the several hundred MBs per synchronization. In this paper, we propose a scheme to reduce network traffic through layer placement that considers the resources that each layer uses. Through analysis of the characteristics of CNN, we find that placement of layers can be done in an effective manner. We then incorporate this observation within the TensorFlow framework such that layers can be automatically placed for more efficient training. Our evaluation making use of this placement scheme show that training time can be significantly reduced without loss of accuracy for many CNN models.

Includes members:
  Member "deep learning based recommender system a survey and new perspectives":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "beyond data and model parallelism for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a federated learning framework for privacy preserving and parallel training")
  Member "gpipe efficient training of giant neural networks using pipeline parallelism":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")
  Member "parameter hub a rack scale parameter server for distributed deep neural network training":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed hierarchical gpu parameter server for massive scale deep learning ads systems")
  Member "horovod fast and easy distributed deep learning in tensorflow":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "pipedream fast and efficient pipeline parallel dnn training":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "training large neural networks with constant memory using a new execution algorithm")
  Member "exploring hidden dimensions in parallelizing convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ordering chaos memory aware scheduling of irregularly wired neural networks for edge devices")
  Member "gradient sparsification for communication efficient distributed optimization":
    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)