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: "exploring the imposition of synaptic precision restrictions for evolutionary synthesis of deep neural networks"
  Abstract: A key contributing factor to incredible success of deep neural networks has been the significant rise on massively parallel computing devices allowing researchers to greatly increase the size and depth of deep neural networks, leading to significant improvements in modeling accuracy. Although deeper, larger, or complex deep neural networks have shown considerable promise, the computational complexity of such networks is a major barrier to utilization in resource-starved scenarios. We explore the synaptogenesis of deep neural networks in the formation of efficient deep neural network architectures within an evolutionary deep intelligence framework, where a probabilistic generative modeling strategy is introduced to stochastically synthesize increasingly efficient yet effective offspring deep neural networks over generations, mimicking evolutionary processes such as heredity, random mutation, and natural selection in a probabilistic manner. In this study, we primarily explore the imposition of synaptic precision restrictions and its impact on the evolutionary synthesis of deep neural networks to synthesize more efficient network architectures tailored for resource-starved scenarios. Experimental results show significant improvements in synaptic efficiency (~10X decrease for GoogLeNet-based DetectNet) and inference speed (>5X increase for GoogLeNet-based DetectNet) while preserving modeling accuracy.

Connected through hyperedge HE-1 (induced by source paper "nature vs nurture the role of environmental resources in evolutionary deep intelligence"):
  with members:
    - "the mating rituals of deep neural networks learning compact feature representations through sexual evolutionary synthesis"
    - "evolution in groups a deeper look at synaptic cluster driven evolution of deep neural networks"
    - "discovery radiomics via evolutionary deep radiomic sequencer discovery for pathologically proven lung cancer detection"
    - "squishednets squishing squeezenet further for edge device scenarios via deep evolutionary synthesis"
    - "deep learning with darwin evolutionary synthesis of deep neural networks"
    - "evolutionary synthesis of deep neural networks via synaptic cluster driven genetic encoding"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "sparsifying neural network connections for face recognition"

Connected through hyperedge HE-2 (induced by source paper "mitigating architectural mismatch during the evolutionary synthesis of deep neural networks"):
  with members:
    - "nature vs nurture the role of environmental resources in evolutionary deep intelligence"
    - "the mating rituals of deep neural networks learning compact feature representations through sexual evolutionary synthesis"
    - "evolution in groups a deeper look at synaptic cluster driven evolution of deep neural networks"
    - "deep learning with darwin evolutionary synthesis of deep neural networks"
    - "evolutionary synthesis of deep neural networks via synaptic cluster driven genetic encoding"

Connected through hyperedge HE-3 (induced by source paper "squishednets squishing squeezenet further for edge device scenarios via deep evolutionary synthesis"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "fixed point optimization of deep neural networks with adaptive step size retraining"
    - "two bit networks for deep learning on resource constrained embedded devices"
    - "quantization and training of low bit width convolutional neural networks for object detection"
    - "ternary weight networks"
    - "deep learning with darwin evolutionary synthesis of deep neural networks"
    - "evolutionary synthesis of deep neural networks via synaptic cluster driven genetic encoding"

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)