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 "empirical evaluation of parallel training algorithms on acoustic modeling"):
  Abstract: Deep learning models (DLMs) are state-of-the-art techniques in speech recognition. However, training good DLMs can be time consuming especially for production-size models and corpora. Although several parallel training algorithms have been proposed to improve training efficiency, there is no clear guidance on which one to choose for the task in hand due to lack of systematic and fair comparison among them. In this paper we aim at filling this gap by comparing four popular parallel training algorithms in speech recognition, namely asynchronous stochastic gradient descent (ASGD), blockwise model-update filtering (BMUF), bulk synchronous parallel (BSP) and elastic averaging stochastic gradient descent (EASGD), on 1000-hour LibriSpeech corpora using feed-forward deep neural networks (DNNs) and convolutional, long short-term memory, DNNs (CLDNNs). Based on our experiments, we recommend using BMUF as the top choice to train acoustic models since it is most stable, scales well with number of GPUs, can achieve reproducible results, and in many cases even outperforms single-GPU SGD. ASGD can be used as a substitute in some cases.

Includes members:
  Member "theano mpi a theano based distributed training framework":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient and robust parallel dnn training through model parallelism on multi gpu platform")
  Member "asynchronous stochastic gradient descent with delay compensation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "shadowsync performing synchronization in the background for highly scalable distributed training")
  Member "advances in very deep convolutional neural networks for lvcsr":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "character level recurrent neural networks in practice comparing training and sampling schemes")
  Member "fast and accurate recurrent neural network acoustic models for speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "small footprint open vocabulary keyword spotting with quantized lstm networks")
  Member "experiments on parallel training of deep neural network using model averaging":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "feedforward sequential memory networks a new structure to learn long term dependency":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "improving gated recurrent unit based acoustic modeling with batch normalization and enlarged context")
  Member "long short term memory based recurrent neural network architectures for large vocabulary speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "few shot deep adversarial learning for video based person re identification")
  Member "parallel training of dnns with natural gradient and parameter averaging":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "variational federated multi task learning")

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.CL(Computation and Language)