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: "accelerating training of deep neural networks via sparse edge processing"
  Abstract: We propose a reconfigurable hardware architecture for deep neural networks (DNNs) capable of online training and inference, which uses algorithmically pre-determined, structured sparsity to significantly lower memory and computational requirements. This novel architecture introduces the notion of edge-processing to provide flexibility and combines junction pipelining and operational parallelization to speed up training. The overall effect is to reduce network complexity by factors up to 30x and training time by up to 35x relative to GPUs, while maintaining high fidelity of inference results. This has the potential to enable extensive parameter searches and development of the largely unexplored theoretical foundation of DNNs. The architecture automatically adapts itself to different network sizes given available hardware resources. As proof of concept, we show results obtained for different bit widths.

Connected through hyperedge HE-1 (induced by source paper "pre defined sparse neural networks with hardware acceleration"):
  with members:
    - "characterizing sparse connectivity patterns in neural networks"
    - "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science"
    - "recursive binary neural network learning model with 2 bit weight storage requirement"
    - "morse code datasets for machine learning"
    - "a highly parallel fpga implementation of sparse neural network training"
    - "revisiting small batch training for deep neural networks"
    - "in datacenter performance analysis of a tensor processing unit"
    - "deep expander networks efficient deep networks from graph theory"

Connected through hyperedge HE-2 (induced by source paper "evolving and understanding sparse deep neural networks using cosine similarity"):
  with members:
    - "parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization"
    - "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science"
    - "multi objective evolutionary federated learning"
    - "in datacenter performance analysis of a tensor processing unit"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "training sparse neural networks"
    - "a topological insight into restricted boltzmann machines"
    - "learning both weights and connections for efficient neural networks"

Connected through hyperedge HE-3 (induced by source paper "neural network training with approximate logarithmic computations"):
  with members:
    - "a pre defined sparse kernel based convolution for deep cnns"
    - "pconv the missing but desirable sparsity in dnn weight pruning for real time execution on mobile devices"
    - "rethinking floating point for deep learning"
    - "emnist an extension of mnist to handwritten letters"
    - "incremental network quantization towards lossless cnns with low precision weights"
    - "convolutional neural networks using logarithmic data representation"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-4 (induced by source paper "blocking and sparsity for optimization of convolution calculation algorithm on gpus"):
  with members:
    - "joint deep modeling of users and items using reviews for recommendation"
    - "speeding up convolutional neural networks by exploiting the sparsity of rectifier units"
    - "disan directional self attention network for rnn cnn free language understanding"
    - "fast algorithms for convolutional neural networks"
    - "cudnn efficient primitives for deep learning"
    - "fast training of convolutional networks through ffts"

Connected through hyperedge HE-5 (induced by source paper "performance optimization of convolution calculation by blocking and sparsity on gpu"):
  with members:
    - "joint deep modeling of users and items using reviews for recommendation"
    - "speeding up convolutional neural networks by exploiting the sparsity of rectifier units"
    - "disan directional self attention network for rnn cnn free language understanding"
    - "fast algorithms for convolutional neural networks"
    - "cudnn efficient primitives for deep learning"
    - "fast training of convolutional networks through ffts"

Connected through hyperedge HE-6 (induced by source paper "characterizing sparse connectivity patterns in neural networks"):
  with members:
    - "interleaver design for deep neural networks"
    - "sparse neural networks topologies"
    - "training sparse neural networks"
    - "densely connected convolutional networks"
    - "deep adaptive network an efficient deep neural network with sparse binary connections"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "morse code datasets for machine learning"):
  with members:
    - "characterizing sparse connectivity patterns in neural networks"
    - "toward geometric deep slam"
    - "using synthetic data to train neural networks is model based reasoning"
    - "interleaver design for deep neural networks"
    - "deep adaptive network an efficient deep neural network with sparse binary connections"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "compressing neural networks with the hashing trick"

Connected through hyperedge HE-8 (induced by source paper "a highly parallel fpga implementation of sparse neural network training"):
  with members:
    - "characterizing sparse connectivity patterns in neural networks"
    - "interleaver design for deep neural networks"
    - "net trim convex pruning of deep neural networks with performance guarantee"
    - "understanding deep learning requires rethinking generalization"
    - "eie efficient inference engine on compressed deep neural network"
    - "deep adaptive network an efficient deep neural network with sparse binary connections"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "deep residual learning for 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.NE(Neural and Evolutionary Computing)