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: "ferminets learning generative machines to generate efficient neural networks via generative synthesis"
  Abstract: The tremendous potential exhibited by deep learning is often offset by architectural and computational complexity, making widespread deployment a challenge for edge scenarios such as mobile and other consumer devices. To tackle this challenge, we explore the following idea: Can we learn generative machines to automatically generate deep neural networks with efficient network architectures? In this study, we introduce the idea of generative synthesis, which is premised on the intricate interplay between a generator-inquisitor pair that work in tandem to garner insights and learn to generate highly efficient deep neural networks that best satisfies operational requirements. What is most interesting is that, once a generator has been learned through generative synthesis, it can be used to generate not just one but a large variety of different, unique highly efficient deep neural networks that satisfy operational requirements. Experimental results for image classification, semantic segmentation, and object detection tasks illustrate the efficacy of generative synthesis in producing generators that automatically generate highly efficient deep neural networks (which we nickname FermiNets) with higher model efficiency and lower computational costs (reaching >10x more efficient and fewer multiply-accumulate operations than several tested state-of-the-art networks), as well as higher energy efficiency (reaching >4x improvements in image inferences per joule consumed on a Nvidia Tegra X2 mobile processor). As such, generative synthesis can be a powerful, generalized approach for accelerating and improving the building of deep neural networks for on-device edge 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"
    - "tiny ssd a tiny single shot detection deep convolutional neural network for real time embedded object detection"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"

Connected through hyperedge HE-2 (induced by source paper "do explanations reflect decisions a machine centric strategy to quantify the performance of explainability algorithms"):
  with members:
    - "human vs machine attention in neural networks a comparative study"
    - "learning explainable models using attribution priors"
    - "a survey on explainable artificial intelligence xai towards medical xai"
    - "probing neural network comprehension of natural language arguments"
    - "adversarial patch"
    - "allennlp a deep semantic natural language processing platform"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "smoothgrad removing noise by adding noise"

Connected through hyperedge HE-3 (induced by source paper "seeing convolution through the eyes of finite transformation semigroup theory an abstract algebraic interpretation of convolutional neural networks"):
  with members:
    - "attonets compact and efficient deep neural networks for the edge via human machine collaborative design"
    - "information theoretic interpretation of deep learning"
    - "rotation equivariant cnns for digital pathology"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "opening the black box of deep neural networks via information"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"

Connected through hyperedge HE-4 (induced by source paper "yolo nano a highly compact you only look once convolutional neural network for object detection"):
  with members:
    - "attonets compact and efficient deep neural networks for the edge via human machine collaborative design"
    - "fairnas rethinking evaluation fairness of weight sharing neural architecture search"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "yolov3 an incremental improvement"
    - "netscore towards universal metrics for large scale performance analysis of deep neural networks for practical on device edge usage"
    - "mnasnet platform aware neural architecture search for mobile"
    - "fast yolo a fast you only look once system for real time embedded object detection in video"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-5 (induced by source paper "deeplabnet end to end learning of deep radial basis networks with fully learnable basis functions"):
  with members:
    - "adaptive blending units trainable activation functions for deep neural networks"
    - "deep rbf networks revisited robust classification with rejection"
    - "deep rbfnet point cloud feature learning using radial basis functions"
    - "squeeze and excitation networks"
    - "searching for activation functions"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "deep learning with darwin evolutionary synthesis of deep neural networks"
    - "neural architecture search with reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "progressive label distillation learning input efficient deep neural networks"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "deep residual learning for small footprint keyword spotting"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "do deep nets really need to be deep"

Connected through hyperedge HE-7 (induced by source paper "state of compact architecture search for deep neural networks"):
  with members:
    - "morphnet fast simple resource constrained structure learning of deep networks"
    - "learning structured sparsity in deep neural networks"
    - "deep learning with darwin evolutionary synthesis of deep neural networks"
    - "designing neural network architectures using reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "human machine collaborative design for accelerated design of compact deep neural networks for autonomous driving"):
  with members:
    - "yolo9000 better faster stronger"
    - "deep speech 2 end to end speech recognition in english and mandarin"
    - "rethinking the inception architecture for computer vision"

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)