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: "edge host partitioning of deep neural networks with feature space encoding for resource constrained internet of things platforms"
  Abstract: This paper introduces partitioning an inference task of a deep neural network between an edge and a host platform in the IoT environment. We present a DNN as an encoding pipeline, and propose to transmit the output feature space of an intermediate layer to the host. The lossless or lossy encoding of the feature space is proposed to enhance the maximum input rate supported by the edge platform and/or reduce the energy of the edge platform. Simulation results show that partitioning a DNN at the end of convolutional (feature extraction) layers coupled with feature space encoding enables significant improvement in the energy-efficiency and throughput over the baseline configurations that perform the entire inference at the edge or at the host.

Connected through hyperedge HE-1 (induced by source paper "relevant features based auxiliary cells for energy efficient detection of natural errors"):
  with members:
    - "natural and adversarial error detection using invariance to image transformations"
    - "designing adaptive neural networks for energy constrained image classification"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "distance based confidence score for neural network classifiers"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"
    - "conditional deep learning for energy efficient and enhanced pattern recognition"
    - "lsun construction of a large scale image dataset using deep learning with humans in the loop"

Connected through hyperedge HE-2 (induced by source paper "edge intelligence paving the last mile of artificial intelligence with edge computing"):
  with members:
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "in edge ai intelligentizing mobile edge computing caching and communication by federated learning"
    - "peer to peer federated learning on graphs"
    - "pcamp performance comparison of machine learning packages on the edges"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "on device federated learning via blockchain and its latency analysis"
    - "federated learning strategies for improving communication efficiency"
    - "communication compression for decentralized training"

Connected through hyperedge HE-3 (induced by source paper "bottlenet an end to end approach for feature compression in device edge co inference systems"):
  with members:
    - "improving device edge cooperative inference of deep learning via 2 step pruning"
    - "bottlenet a deep learning architecture for intelligent mobile cloud computing services"
    - "jointdnn an efficient training and inference engine for intelligent mobile cloud computing services"
    - "deep feature compression for collaborative object detection"
    - "neural joint source channel coding"
    - "jalad joint accuracy and latency aware deep structure decoupling for edge cloud execution"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "improving device edge cooperative inference of deep learning via 2 step pruning"):
  with members:
    - "jointdnn an efficient training and inference engine for intelligent mobile cloud computing services"
    - "edge intelligence on demand deep learning model co inference with device edge synergy"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "channel pruning for accelerating very deep neural networks"
    - "pruning convolutional neural networks for resource efficient inference"
    - "eie efficient inference engine on compressed deep neural network"

Connected through hyperedge HE-5 (induced by source paper "a survey on methods and theories of quantized neural networks"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "training and inference with integers in deep neural networks"
    - "model compression via distillation and quantization"
    - "loss aware weight quantization of deep networks"
    - "minimum energy quantized neural networks"
    - "network sketching exploiting binary structure in deep cnns"
    - "wrpn wide reduced precision networks"
    - "the high dimensional geometry of binary neural networks"

Connected through hyperedge HE-6 (induced by source paper "designing adaptive neural networks for energy constrained image classification"):
  with members:
    - "tree cnn a deep convolutional neural network for lifelong learning"
    - "adaptive neural networks for efficient inference"
    - "neuralpower predict and deploy energy efficient convolutional neural networks"
    - "morphnet fast simple resource constrained structure learning of deep networks"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "hyperpower power and memory constrained hyper parameter optimization for neural networks"
    - "nest a neural network synthesis tool based on a grow and prune paradigm"
    - "neural networks designing neural networks multi objective hyper parameter optimization"

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.CV(Computer Vision and Pattern Recognition)