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: "optimizing memory efficiency for deep convolutional neural networks on gpus"
  Abstract: Leveraging large data sets, deep Convolutional Neural Networks (CNNs) achieve state-of-the-art recognition accuracy. Due to the substantial compute and memory operations, however, they require significant execution time. The massive parallel computing capability of GPUs make them as one of the ideal platforms to accelerate CNNs and a number of GPU-based CNN libraries have been developed. While existing works mainly focus on the computational efficiency of CNNs, the memory efficiency of CNNs have been largely overlooked. Yet CNNs have intricate data structures and their memory behavior can have significant impact on the performance. In this work, we study the memory efficiency of various CNN layers and reveal the performance implication from both data layouts and memory access patterns. Experiments show the universal effect of our proposed optimizations on both single layers and various networks, with up to 27.9x for a single layer and up to 5.6x on the whole networks.

Connected through hyperedge HE-1 (induced by source paper "marvel a decoupled model driven approach for efficiently mapping convolutions on spatial dnn accelerators"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "tvm an automated end to end optimizing compiler for deep learning"
    - "eyeriss v2 a flexible accelerator for emerging deep neural networks on mobile devices"
    - "in datacenter performance analysis of a tensor processing unit"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "a systematic approach to blocking convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "hardnet a low memory traffic network"):
  with members:
    - "log densenet how to sparsify a densenet"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "scale sim systolic cnn accelerator simulator"
    - "sparsely aggregated convolutional networks"
    - "single shot refinement neural network for object detection"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "nullhop a flexible convolutional neural network accelerator based on sparse representations of feature maps"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"

Connected through hyperedge HE-3 (induced by source paper "enabling highly efficient capsule networks processing through a pim based architecture design"):
  with members:
    - "optimally scheduling cnn convolutions for efficient memory access"
    - "capsule neural network based height classification using low cost automotive ultrasonic sensors"
    - "high throughput cnn inference on embedded arm big little multi core processors"
    - "beyond the memory wall a case for memory centric hpc system for deep learning"
    - "an overview of deep learning in medical imaging focusing on mri"
    - "capsnet comparative performance evaluation for image classification"
    - "fast capsnet for lung cancer screening"
    - "origami a heterogeneous split architecture for in memory acceleration of learning"

Connected through hyperedge HE-4 (induced by source paper "delta gpu performance model for deep learning applications with in depth memory system traffic analysis"):
  with members:
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "gpgpu performance estimation with core and memory frequency scaling"
    - "a fast rcnn hard positive generation via adversary for object detection"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "dissecting gpu memory hierarchy through microbenchmarking"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "runtime concurrency control and operation scheduling for high performance neural network training"):
  with members:
    - "input aware auto tuning of compute bound hpc kernels"
    - "ray a distributed framework for emerging ai applications"
    - "scaling deep learning on gpu and knights landing clusters"
    - "device placement optimization with reinforcement learning"
    - "tensorflow agents efficient batched reinforcement learning in tensorflow"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep residual learning for image recognition"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"

Connected through hyperedge HE-6 (induced by source paper "scaling deep learning on gpu and knights landing clusters"):
  with members:
    - "revisiting distributed synchronous sgd"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "deep residual learning for image recognition"
    - "deep learning with limited numerical precision"
    - "firecaffe near linear acceleration of deep neural network training on compute clusters"
    - "going deeper with convolutions"

Connected through hyperedge HE-7 (induced by source paper "handwritten isolated bangla compound character recognition a new benchmark using a novel deep learning approach"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "theano a python framework for fast computation of mathematical expressions"
    - "very deep multilingual convolutional neural networks for lvcsr"
    - "equilibrated adaptive learning rates for non convex optimization"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

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)