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: "virtualizing deep neural networks for memory efficient neural network design"
  Abstract: The most widely used machine learning frameworks require users to carefully tune their memory usage so that the deep neural network (DNN) fits into the DRAM capacity of a GPU. This restriction hampers a researcher's flexibility to study different machine learning algorithms, forcing them to either use a less desirable network architecture or parallelize the processing across multiple GPUs. We propose a runtime memory manager that virtualizes the memory usage of DNNs such that both GPU and CPU memory can simultaneously be utilized for training larger DNNs. Our virtualized DNN (vDNN) reduces the average memory usage of AlexNet by 61% and OverFeat by 83%, a significant reduction in memory requirements of DNNs. Similar experiments on VGG-16, one of the deepest and memory hungry DNNs to date, demonstrate the memory-efficiency of our proposal. vDNN enables VGG-16 with batch size 256 (requiring 28 GB of memory) to be trained on a single NVIDIA K40 GPU card containing 12 GB of memory, with 22% performance loss compared to a hypothetical GPU with enough memory to hold the entire DNN.

Connected through hyperedge HE-1 (induced by source paper "scaling back propagation by parallel scan algorithm"):
  with members:
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "tbd benchmarking and analyzing deep neural network training"
    - "channel pruning for accelerating very deep neural networks"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"
    - "vdnn virtualized deep neural networks for scalable memory efficient neural network design"
    - "compression of neural machine translation models via pruning"
    - "learning both weights and connections for efficient neural networks"

Connected through hyperedge HE-2 (induced by source paper "auto precision scaling for distributed deep learning"):
  with members:
    - "rethinking floating point for deep learning"
    - "image classification at supercomputer scale"
    - "training deep neural networks with 8 bit floating point numbers"
    - "imagenet training in minutes"
    - "scaling sgd batch size to 32k for imagenet training"
    - "extremely large minibatch sgd training resnet 50 on imagenet in 15 minutes"
    - "training deep nets with sublinear memory cost"
    - "on large batch training for deep learning generalization gap and sharp minima"

Connected through hyperedge HE-3 (induced by source paper "mxnet mpi embedding mpi parallelism in parameter server task model for scaling deep learning"):
  with members:
    - "revisiting distributed synchronous sgd"
    - "deepspark a spark based distributed deep learning framework for commodity clusters"
    - "an overview of gradient descent optimization algorithms"
    - "tensorflow a system for large scale machine learning"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "deep learning with elastic averaging sgd"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-4 (induced by source paper "vdnn virtualized deep neural networks for scalable memory efficient neural network design"):
  with members:
    - "deep networks with stochastic depth"
    - "eie efficient inference engine on compressed deep neural network"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "comparative study of caffe neon theano and torch for deep learning"
    - "deep residual learning for image recognition"
    - "reduced precision strategies for bounded memory in deep neural nets"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"

Connected through hyperedge HE-5 (induced by source paper "theano a python framework for fast computation of mathematical expressions"):
  with members:
    - "training deep nets with sublinear memory cost"
    - "blocks and fuel frameworks for deep learning"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "one weird trick for parallelizing convolutional neural networks"
    - "deep learning with elastic averaging sgd"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-6 (induced by source paper "training deep nets with sublinear memory cost"):
  with members:
    - "identity mappings in deep residual networks"
    - "vdnn virtualized deep neural networks for scalable memory efficient neural network design"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "highway long short term memory rnns for distant speech recognition"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "training very deep networks"
    - "theano new features and speed improvements"

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)