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: "purine a bi graph based deep learning framework"
  Abstract: In this paper, we introduce a novel deep learning framework, termed Purine. In Purine, a deep network is expressed as a bipartite graph (bi-graph), which is composed of interconnected operators and data tensors. With the bi-graph abstraction, networks are easily solvable with event-driven task dispatcher. We then demonstrate that different parallelism schemes over GPUs and/or CPUs on single or multiple PCs can be universally implemented by graph composition. This eases researchers from coding for various parallelization schemes, and the same dispatcher can be used for solving variant graphs. Scheduled by the task dispatcher, memory transfers are fully overlapped with other computations, which greatly reduce the communication overhead and help us achieve approximate linear acceleration.

Connected through hyperedge HE-1 (induced by source paper "benchmarking contemporary deep learning hardware and frameworks a survey of qualitative metrics"):
  with members:
    - "analysis of dawnbench a time to accuracy machine learning performance benchmark"
    - "bigdl a distributed deep learning framework for big data"
    - "improving data quality through deep learning and statistical models"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "chainermn scalable distributed deep learning framework"
    - "deep learning on fpgas past present and future"
    - "theano a python framework for fast computation of mathematical expressions"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-2 (induced by source paper "benchmarking deep learning hardware and frameworks qualitative metrics"):
  with members:
    - "bigdl a distributed deep learning framework for big data"
    - "improving data quality through deep learning and statistical models"
    - "chainermn scalable distributed deep learning framework"
    - "deep learning on fpgas past present and future"
    - "theano a python framework for fast computation of mathematical expressions"
    - "blocks and fuel frameworks for deep learning"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-3 (induced by source paper "exploring the design space of deep convolutional neural networks at large scale"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "origami a 803 gop s w convolutional network accelerator"
    - "systematic evaluation of convolution neural network advances on the imagenet"
    - "theano mpi a theano based distributed training framework"
    - "distributed deep learning using synchronous stochastic gradient descent"
    - "ristretto hardware oriented approximation of convolutional neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "how to scale distributed deep learning"

Connected through hyperedge HE-4 (induced by source paper "theano mpi a theano based distributed training framework"):
  with members:
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "firecaffe near linear acceleration of deep neural network training on compute clusters"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "theano based large scale visual recognition with multiple gpus"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"):
  with members:
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "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.NE(Neural and Evolutionary Computing)