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 "hypar towards hybrid parallelism for deep learning accelerator array"):
  Abstract: With the rise of artificial intelligence in recent years, Deep Neural Networks (DNNs) have been widely used in many domains. To achieve high performance and energy efficiency, hardware acceleration (especially inference) of DNNs is intensively studied both in academia and industry. However, we still face two challenges: large DNN models and datasets, which incur frequent off-chip memory accesses; and the training of DNNs, which is not well-explored in recent accelerator designs. To truly provide high throughput and energy efficient acceleration for the training of deep and large models, we inevitably need to use multiple accelerators to explore the coarse-grain parallelism, compared to the fine-grain parallelism inside a layer considered in most of the existing architectures. It poses the key research question to seek the best organization of computation and dataflow among accelerators. In this paper, we propose a solution HyPar to determine layer-wise parallelism for deep neural network training with an array of DNN accelerators. HyPar partitions the feature map tensors (input and output), the kernel tensors, the gradient tensors, and the error tensors for the DNN accelerators. A partition constitutes the choice of parallelism for weighted layers. The optimization target is to search a partition that minimizes the total communication during training a complete DNN. To solve this problem, we propose a communication model to explain the source and amount of communications. Then, we use a hierarchical layer-wise dynamic programming method to search for the partition for each layer.

Includes members:
  Member "supporting very large models using automatic dataflow graph partitioning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "heterogeneity aware asynchronous decentralized training")
  Member "red a reram based deconvolution accelerator":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an overview of in memory processing with emerging non volatile memory for data intensive applications")
  Member "mt spike a multilayer time based spiking neuromorphic architecture with temporal error backpropagation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep learning in spiking neural networks")
  Member "pt spike a precise time dependent single spike neuromorphic architecture with efficient supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deepn jpeg a deep neural network favorable jpeg based image compression framework")
  Member "ucnn exploiting computational reuse in deep neural networks via weight repetition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "non structured dnn weight pruning is it beneficial in any platform")
  Member "vibnn hardware acceleration of bayesian neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "exploiting oxide based resistive ram variability for bayesian neural network hardware design")
  Member "revisiting small batch training for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hyper parameter optimization a review of algorithms and applications")
  Member "unifying data model and hybrid parallelism in deep learning via tensor tiling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hybrid data model parallel training for sequence to sequence recurrent neural network machine translation")

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)