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: "on chip communication network for efficient training of deep convolutional networks on heterogeneous manycore systems"
  Abstract: Convolutional Neural Networks (CNNs) have shown a great deal of success in diverse application domains including computer vision, speech recognition, and natural language processing. However, as the size of datasets and the depth of neural network architectures continue to grow, it is imperative to design high-performance and energy-efficient computing hardware for training CNNs. In this paper, we consider the problem of designing specialized CPU-GPU based heterogeneous manycore systems for energy-efficient training of CNNs. It has already been shown that the typical on-chip communication infrastructures employed in conventional CPU-GPU based heterogeneous manycore platforms are unable to handle  both  CPU and GPU communication requirements efficiently. To address this issue, we first analyze the on-chip traffic patterns that arise from the computational processes associated with training two deep CNN architectures, namely, LeNet and CDBNet, to perform image classification. By leveraging this knowledge, we design a hybrid Network-on-Chip (NoC) architecture, which consists of both wireline and wireless links, to improve the performance of CPU-GPU based heterogeneous manycore platforms running the above-mentioned CNN training workloads. The proposed NoC achieves  1.8×  reduction in network latency and improves the network throughput by a factor of  2.2  for training CNNs, when compared to a highly-optimized wireline mesh NoC. For the considered CNN workloads, these network-level improvements translate into  25  percent savings in full-system energy-delay-product (EDP). This demonstrates that the proposed hybrid NoC for heterogeneous manycore architectures is capable of significantly accelerating training of CNNs while remaining energy-efficient.

Connected through hyperedge HE-1 (induced by source paper "alleviating bottlenecks for dnn execution on gpus via opportunistic computing"):
  with members:
    - "high performance zero memory overhead direct convolutions"
    - "one weird trick for parallelizing convolutional neural networks"
    - "fast convolutional nets with fbfft a gpu performance evaluation"
    - "caffe convolutional architecture for fast feature embedding"
    - "cudnn efficient primitives for deep learning"

Connected through hyperedge HE-2 (induced by source paper "ring mesh a scalable and high performance approach for manycore accelerators"):
  with members:
    - "application of deep convolutional neural networks for detecting extreme weather in climate datasets"

Connected through hyperedge HE-3 (induced by source paper "learning based application agnostic 3d noc design for heterogeneous manycore systems"):
  with members:
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-4 (induced by source paper "engineer the channel and adapt to it enabling wireless intra chip communication"):
  with members:
    - "millimeter wave propagation within a computer chip package"
    - "medium access control in wireless network on chip a context analysis"
    - "channel characterization for chip scale wireless communications within computing packages"

Connected through hyperedge HE-5 (induced by source paper "channel characterization for chip scale wireless communications within computing packages"):
  with members:
    - "millimeter wave propagation within a computer chip package"
    - "medium access control in wireless network on chip a context analysis"
    - "modeling the em field distribution within a computer chip package"

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)