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: "nestdnn resource aware multi tenant on device deep learning for continuous mobile vision"
  Abstract: Mobile vision systems such as smartphones, drones, and augmented-reality headsets are revolutionizing our lives. These systems usually run multiple applications concurrently and their available resources at runtime are dynamic due to events such as starting new applications, closing existing applications, and application priority changes. In this paper, we present NestDNN, a framework that takes the dynamics of runtime resources into account to enable resource-aware multi-tenant on-device deep learning for mobile vision systems. NestDNN enables each deep learning model to offer flexible resource-accuracy trade-offs. At runtime, it dynamically selects the optimal resource-accuracy trade-off for each deep learning model to fit the model's resource demand to the system's available runtime resources. In doing so, NestDNN efficiently utilizes the limited resources in mobile vision systems to jointly maximize the performance of all the concurrently running applications. Our experiments show that compared to the resource-agnostic status quo approach, NestDNN achieves as much as 4.2% increase in inference accuracy, 2.0× increase in video frame processing rate and 1.7× reduction on energy consumption.

Connected through hyperedge HE-1 (induced by source paper "mdldroid a chainsgd reduce approach to mobile deep learning for personal mobile sensing"):
  with members:
    - "a distributed synchronous sgd algorithm with global top k sparsification for low bandwidth networks"
    - "deep learning on mobile devices a review"
    - "layered sgd a decentralized and synchronous sgd algorithm for scalable deep neural network training"
    - "towards federated learning at scale system design"
    - "multi modality sensor data classification with selective attention"
    - "analyzing federated learning through an adversarial lens"
    - "deep learning towards mobile applications"
    - "edge intelligence on demand deep learning model co inference with device edge synergy"

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 "attonets compact and efficient deep neural networks for the edge via human machine collaborative design"):
  with members:
    - "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"
    - "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"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"

Connected through hyperedge HE-4 (induced by source paper "characterizing the deep neural networks inference performance of mobile applications"):
  with members:
    - "no dnn left behind improving inference in the cloud with multi tenancy"
    - "deepcache principled cache for mobile deep vision"
    - "a first look at deep learning apps on smartphones"
    - "serving deep learning models in a serverless platform"
    - "learning transferable architectures for scalable image recognition"
    - "tensorflow serving flexible high performance ml serving"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"

Connected through hyperedge HE-5 (induced by source paper "disentangling redundancy for multi task pruning"):
  with members:
    - "branched multi task networks deciding what layers to share"
    - "multi task zipping via layer wise neuron sharing"
    - "unifying and merging well trained deep neural networks for inference stage"
    - "compressing neural networks using the variational information bottleneck"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "estimating mixture entropy with pairwise distances"
    - "learning structured sparsity in deep neural networks"
    - "eie efficient inference engine on compressed deep neural network"

Connected through hyperedge HE-6 (induced by source paper "preventing information leakage with neural architecture search"):
  with members:
    - "exploiting unintended feature leakage in collaborative learning"
    - "beyond inferring class representatives user level privacy leakage from federated learning"
    - "not just privacy improving performance of private deep learning in mobile cloud"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "n2n learning network to network compression via policy gradient reinforcement learning"
    - "membership inference attacks against machine learning models"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "inverting visual representations with convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "rexcam resource efficient cross camera video analytics at scale"):
  with members:
    - "scaling video analytics systems to large camera deployments"
    - "features for multi target multi camera tracking and re identification"
    - "large scale distributed neural network training through online distillation"
    - "focus querying large video datasets with low latency and low cost"
    - "object detection by spatio temporal analysis and tracking of the detected objects in a video with variable background"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "end to end flow correlation tracking with spatial temporal attention"
    - "noscope optimizing neural network queries over video at scale"

Connected through hyperedge HE-8 (induced by source paper "scaling video analytics systems to large camera deployments"):
  with members:
    - "person re identification by camera correlation aware feature augmentation"
    - "features for multi target multi camera tracking and re identification"
    - "focus querying large video datasets with low latency and low cost"
    - "unified framework for automated person re identification and camera network topology inference in camera networks"
    - "noscope optimizing neural network queries over video at scale"
    - "person re identification in the wild"
    - "performance measures and a data set for multi target multi camera tracking"
    - "fast video classification via adaptive cascading of deep models"

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)