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: "two bit networks for deep learning on resource constrained embedded devices"
  Abstract: With the rapid proliferation of Internet of Things and intelligent edge devices, there is an increasing need for implementing machine learning algorithms, including deep learning, on resource-constrained mobile embedded devices with limited memory and computation power. Typical large Convolutional Neural Networks (CNNs) need large amounts of memory and computational power, and cannot be deployed on embedded devices efficiently. We present Two-Bit Networks (TBNs) for model compression of CNNs with edge weights constrained to (-2, -1, 1, 2), which can be encoded with two bits. Our approach can reduce the memory usage and improve computational efficiency significantly while achieving good performance in terms of classification accuracy, thus representing a reasonable tradeoff between model size and performance.

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 "jsdoop and tensorflow js volunteer distributed web browser based neural network training"):
  with members:
    - "tensorflow js machine learning for the web and beyond"
    - "towards federated learning at scale system design"
    - "moving deep learning into web browser how far can we go"
    - "boinc a platform for volunteer computing"
    - "deep learning towards mobile applications"
    - "deep learning in mobile and wireless networking a survey"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "sparse communication for distributed gradient descent"

Connected through hyperedge HE-4 (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"
    - "squishednets squishing squeezenet further for edge device scenarios via deep evolutionary synthesis"
    - "projectionnet learning efficient on device deep networks using neural projections"

Connected through hyperedge HE-5 (induced by source paper "an overview of datatype quantization techniques for convolutional neural networks"):
  with members:
    - "fixed point optimization of deep neural networks with adaptive step size retraining"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "ternary weight networks"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "fixed point quantization of deep convolutional networks"
    - "deep residual learning for image recognition"
    - "compressing neural networks with the hashing trick"

Connected through hyperedge HE-6 (induced by source paper "bitnet bit regularized deep neural networks"):
  with members:
    - "soft to hard vector quantization for end to end learning compressible representations"
    - "mixed low precision deep learning inference using dynamic fixed point"
    - "alternating direction method of multipliers for sparse convolutional neural networks"
    - "fixed point optimization of deep neural networks with adaptive step size retraining"
    - "towards the limit of network quantization"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "deep neural networks are robust to weight binarization and other non linear distortions"
    - "understanding the impact of precision quantization on the accuracy and energy of neural networks"

Connected through hyperedge HE-7 (induced by source paper "squishednets squishing squeezenet further for edge device scenarios via deep evolutionary synthesis"):
  with members:
    - "exploring the imposition of synaptic precision restrictions for evolutionary synthesis of deep neural networks"
    - "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"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "fog assisted wiot a smart fog gateway for end to end analytics in wearable internet of things"):
  with members:
    - "bigear inferring the ambient and emotional correlates from smartphone based acoustic big data"
    - "fit a fog computing device for speech teletreatments"
    - "echowear smartwatch technology for voice and speech treatments of patients with parkinson s disease"
    - "fog data enhancing telehealth big data through fog computing"

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.LG(Machine Learning)