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: "distributed deep neural networks over the cloud the edge and end devices"
  Abstract: We propose distributed deep neural networks (DDNNs) over distributed computing hierarchies, consisting of the cloud, the edge (fog) and end devices. While being able to accommodate inference of a deep neural network (DNN) in the cloud, a DDNN also allows fast and localized inference using shallow portions of the neural network at the edge and end devices. When supported by a scalable distributed computing hierarchy, a DDNN can scale up in neural network size and scale out in geographical span. Due to its distributed nature, DDNNs enhance sensor fusion, system fault tolerance and data privacy for DNN applications. In implementing a DDNN, we map sections of a DNN onto a distributed computing hierarchy. By jointly training these sections, we minimize communication and resource usage for devices and maximize usefulness of extracted features which are utilized in the cloud. The resulting system has built-in support for automatic sensor fusion and fault tolerance. As a proof of concept, we show a DDNN can exploit geographical diversity of sensors to improve object recognition accuracy and reduce communication cost. In our experiment, compared with the traditional method of offloading raw sensor data to be processed in the cloud, DDNN locally processes most sensor data on end devices while achieving high accuracy and is able to reduce the communication cost by a factor of over 20x.

Connected through hyperedge HE-1 (induced by source paper "run time deep model multiplexing"):
  with members:
    - "bottlenet a deep learning architecture for intelligent mobile cloud computing services"
    - "towards collaborative intelligence friendly architectures for deep learning"
    - "jointdnn an efficient training and inference engine for intelligent mobile cloud computing services"
    - "deep feature compression for collaborative object detection"
    - "mnasnet platform aware neural architecture search for mobile"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "mdinference balancing inference accuracy andlatency for mobile applications"):
  with members:
    - "benchmark analysis of representative deep neural network architectures"
    - "learning transferable architectures for scalable image recognition"
    - "branchynet fast inference via early exiting from deep neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "cloud based or on device an empirical study of mobile deep inference"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "sep nets small and effective pattern networks"
    - "clipper a low latency online prediction serving system"

Connected through hyperedge HE-3 (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"
    - "edge host partitioning of deep neural networks with feature space encoding for resource constrained internet of things platforms"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "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-4 (induced by source paper "fastva deep learning video analytics through edge processing and npu in mobile"):
  with members:
    - "exploiting feature and class relationships in video categorization with regularized deep neural networks"
    - "fastdeepiot towards understanding and optimizing neural network execution time on mobile and embedded devices"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "cnndroid gpu accelerated execution of trained deep convolutional neural networks on android"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "going deeper with convolutions"

Connected through hyperedge HE-5 (induced by source paper "failout achieving failure resilient inference in distributed neural networks"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "identity mappings in deep residual networks"
    - "measuring neural net robustness with constraints"
    - "zoneout regularizing rnns by randomly preserving hidden activations"
    - "tensorflow a system for large scale machine learning"
    - "deep residual learning for image recognition"
    - "imagenet large scale visual recognition challenge"
    - "sequence to sequence learning with neural networks"

Connected through hyperedge HE-6 (induced by source paper "adaptive anomaly detection for iot data in hierarchical edge computing"):
  with members:
    - "distributed anomaly detection using autoencoder neural networks in wsn for iot"
    - "deep learning for iot big data and streaming analytics a survey"
    - "blockdrop dynamic inference paths in residual networks"
    - "branchynet fast inference via early exiting from deep neural networks"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-7 (induced by source paper "improving iot analytics through selective edge execution"):
  with members:
    - "analytics for the internet of things a survey"
    - "dynamic deep neural networks optimizing accuracy efficiency trade offs by selective execution"
    - "a survey on mobile edge computing the communication perspective"
    - "compression of deep neural networks for image instance retrieval"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-8 (induced by source paper "artificial intelligence for digital agriculture at scale techniques policies and challenges"):
  with members:
    - "auto tuning neural network quantization framework for collaborative inference between the cloud and edge"
    - "privacy preserving machine learning through data obfuscation"
    - "edge cloud offloading algorithms issues methods and perspectives"
    - "low power wide area networks an overview"

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)