human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "partitioning compute units in cnn acceleration for statistical memory traffic shaping"
  Abstract: Convolutional Neural Networks (CNNs) have become the default choice for processing visual information, and the design complexity of CNNs has been steadily increasing to improve accuracy. To cope with the massive amount of computation needed for such complex CNNs, the latest solutions utilize blocking of an image over the available dimensions (e.g., horizontal, vertical, channel, and kernel) and batching of multiple input images to improve data reuse in the memory hierarchy. While there has been a large collection of works on maximizing data reuse, only a few studies have focused on the memory bottleneck problem caused by limited bandwidth. Bandwidth bottleneck can easily occur in CNN acceleration as CNN layers have different sizes with varying computation needs and as batching is typically performed over each layer of CNN for an ideal data reuse. In this case, the data transfer demand for a layer can be relatively low or high compared to the computation requirement of the layer, and therefore temporal fluctuations in memory access can be induced eventually causing bandwidth problems. In this paper, we first show that there exists a high degree of fluctuation in memory access to computation ratio depending on CNN layers and functions in the layer being processed by the compute units (cores), where the compute units are tightly synchronized to maximize data reuse. Then we propose a strategy of partitioning the compute units where the cores within each partition process a batch of input data in a synchronous manner to maximize data reuse but different partitions run asynchronously. Because the partitions stay asynchronous and typically process different CNN layers at any given moment, the memory access traffic sizes of the partitions become statistically shuffled. Thus, the partitioning of compute units and asynchronous use of them make the total memory access traffic size be smoothened over time, and the degree of partitioning determines a tradeoff between data reuse efficiency and memory bandwidth utilization efficiency. We call this smoothing statistical memory traffic shaping, and we show that it can lead to 8.0 percent of performance gain on a commercial 64-core processor when running ResNet-50.

Connected through hyperedge HE-1 (induced by source paper "on linear learning with manycore processors"):
  with members:
    - "parallel approximation of the maximum likelihood estimation for the prediction of large scale geostatistics simulations"
    - "faster coordinate descent via adaptive importance sampling"
    - "scaling deep learning on gpu and knights landing clusters"
    - "efficient use of limited memory accelerators for linear learning on heterogeneous systems"
    - "safe adaptive importance sampling"
    - "primal dual rates and certificates"
    - "tensorflow a system for large scale machine learning"
    - "passcode parallel asynchronous stochastic dual co ordinate descent"

Connected through hyperedge HE-2 (induced by source paper "restructuring batch normalization to accelerate cnn training"):
  with members:
    - "understanding batch normalization"
    - "in datacenter performance analysis of a tensor processing unit"
    - "an analysis of deep neural network models for practical applications"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "a systematic approach to blocking convolutional neural networks"
    - "aggregated residual transformations for deep neural networks"

Question: Please tell me which class the center node 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)