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: "squishednets squishing squeezenet further for edge device scenarios via deep evolutionary synthesis"
  Abstract: While deep neural networks have been shown in recent years to outperform other machine learning methods in a wide range of applications, one of the biggest challenges with enabling deep neural networks for widespread deployment on edge devices such as mobile and other consumer devices is high computational and memory requirements. Recently, there has been greater exploration into small deep neural network architectures that are more suitable for edge devices, with one of the most popular architectures being SqueezeNet, with an incredibly small model size of 4.8MB. Taking further advantage of the notion that many applications of machine learning on edge devices are often characterized by a low number of target classes, this study explores the utility of combining architectural modifications and an evolutionary synthesis strategy for synthesizing even smaller deep neural architectures based on the more recent SqueezeNet v1.1 macroarchitecture for applications with fewer target classes. In particular, architectural modifications are first made to SqueezeNet v1.1 to accommodate for a 10-class ImageNet-10 dataset, and then an evolutionary synthesis strategy is leveraged to synthesize more efficient deep neural networks based on this modified macroarchitecture. The resulting SquishedNets possess model sizes ranging from 2.4MB to 0.95MB (~5.17X smaller than SqueezeNet v1.1, or 253X smaller than AlexNet). Furthermore, the SquishedNets are still able to achieve accuracies ranging from 81.2% to 77%, and able to process at speeds of 156 images/sec to as much as 256 images/sec on a Nvidia Jetson TX1 embedded chip. These preliminary results show that a combination of architectural modifications and an evolutionary synthesis strategy can be a useful tool for producing very small deep neural network architectures that are well-suited for edge device scenarios.

Connected through hyperedge HE-1 (induced by source paper "attonets compact and efficient deep neural networks for the edge via human machine collaborative design"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "nestdnn resource aware multi tenant on device deep learning for continuous mobile vision"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "netscore towards universal metrics for large scale performance analysis of deep neural networks for practical on device edge usage"
    - "monas multi objective neural architecture search using reinforcement learning"
    - "mnasnet platform aware neural architecture search for mobile"
    - "ferminets learning generative machines to generate efficient neural networks via generative synthesis"
    - "tiny ssd a tiny single shot detection deep convolutional neural network for real time embedded object detection"

Connected through hyperedge HE-2 (induced by source paper "edgesegnet a compact network for semantic segmentation"):
  with members:
    - "attonets compact and efficient deep neural networks for the edge via human machine collaborative design"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "netscore towards universal metrics for large scale performance analysis of deep neural networks for practical on device edge usage"
    - "monas multi objective neural architecture search using reinforcement learning"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "mnasnet platform aware neural architecture search for mobile"
    - "tiny ssd a tiny single shot detection deep convolutional neural network for real time embedded object detection"

Connected through hyperedge HE-3 (induced by source paper "netscore towards universal metrics for large scale performance analysis of deep neural networks for practical on device edge usage"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "regularized evolution for image classifier architecture search"
    - "munet a highly compact deep convolutional neural network architecture for real time embedded traffic sign classification"
    - "tiny ssd a tiny single shot detection deep convolutional neural network for real time embedded object detection"
    - "squeezenext hardware aware neural network design"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "progressive neural architecture search"
    - "an analysis of deep neural network models for practical applications"

Connected through hyperedge HE-4 (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"
    - "exploring the imposition of synaptic precision restrictions for evolutionary synthesis of deep neural networks"
    - "discovery radiomics via evolutionary deep radiomic sequencer discovery for pathologically proven lung cancer detection"
    - "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-5 (induced by source paper "ferminets learning generative machines to generate efficient neural networks via generative synthesis"):
  with members:
    - "netscore towards universal metrics for large scale performance analysis of deep neural networks for practical on device edge usage"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "an analysis of deep neural network models for practical applications"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "two bit networks for deep learning on resource constrained embedded devices"
    - "projectionnet learning efficient on device deep networks using neural projections"

Connected through hyperedge HE-6 (induced by source paper "munet a highly compact deep convolutional neural network architecture for real time embedded traffic sign classification"):
  with members:
    - "netscore towards universal metrics for large scale performance analysis of deep neural networks for practical on device edge usage"
    - "towards principled design of deep convolutional networks introducing simpnet"
    - "tiny ssd a tiny single shot detection deep convolutional neural network for real time embedded object detection"
    - "an analysis of deep neural network models for practical applications"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "squeezedet unified small low power fully convolutional neural networks for real time object detection for autonomous driving"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

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)