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: "noscope optimizing neural network queries over video at scale"
  Abstract: Recent advances in computer vision-in the form of deep neural networks-have made it possible to query increasing volumes of video data with high accuracy. However, neural network inference is computationally expensive at scale: applying a state-of-the-art object detector in real time (i.e., 30+ frames per second) to a single video requires a $4000 GPU. In response, we present NoScope, a system for querying videos that can reduce the cost of neural network video analysis by up to three orders of magnitude via inference-optimized model search. Given a target video, object to detect, and reference neural network, NoScope automatically searches for and trains a sequence, or cascade, of models that preserves the accuracy of the reference network but is specialized to the target video and are therefore far less computationally expensive. NoScope cascades two types of models: specialized models that forego the full generality of the reference model but faithfully mimic its behavior for the target video and object; and difference detectors that highlight temporal differences across frames. We show that the optimal cascade architecture differs across videos and objects, so NoScope uses an efficient cost-based optimizer to search across models and cascades. With this approach, NoScope achieves two to three order of magnitude speed-ups (265-15,500x real-time) on binary classification tasks over fixed-angle webcam and surveillance video while maintaining accuracy within 1-5% of state-of-the-art neural networks.

Connected through hyperedge HE-1 (induced by source paper "video monitoring queries"):
  with members:
    - "approximate query processing using deep generative models"
    - "multi attribute selectivity estimation using deep learning"
    - "yolov3 an incremental improvement"
    - "blazeit fast exploratory video queries using neural networks"
    - "verdictdb universalizing approximate query processing"
    - "focus querying large video datasets with low latency and low cost"
    - "towards a hands free query optimizer through deep learning"
    - "the case for learned index structures"

Connected through hyperedge HE-2 (induced by source paper "model assertions for monitoring and improving ml model"):
  with members:
    - "better ai through logical scaffolding"
    - "continuous integration of machine learning models with ease ml ci towards a rigorous yet practical treatment"
    - "outside the box abstraction based monitoring of neural networks"
    - "scaling video analytics on constrained edge nodes"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "vstore a data store for analytics on large videos"
    - "selection via proxy efficient data selection for deep learning"
    - "formal security analysis of neural networks using symbolic intervals"

Connected through hyperedge HE-3 (induced by source paper "finding interesting frames in deep video analytics a top k approach"):
  with members:
    - "blazeit optimizing declarative aggregation and limit queries for neural network based video analytics"
    - "yolov3 an incremental improvement"
    - "focus querying large video datasets with low latency and low cost"
    - "scanner efficient video analysis at scale"
    - "occlusion aware hand pose estimation using hierarchical mixture density network"
    - "face detection using deep learning an improved faster rcnn approach"
    - "abnormality detection and localization in chest x rays using deep convolutional neural networks"
    - "mask r cnn"

Connected through hyperedge HE-4 (induced by source paper "approximate query service on autonomous iot cameras"):
  with members:
    - "crowd counting with decomposed uncertainty"
    - "scaling video analytics on constrained edge nodes"
    - "vstore a data store for analytics on large videos"
    - "supporting video queries on zero streaming cameras"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "yolov3 an incremental improvement"
    - "blazeit fast exploratory video queries using neural networks"
    - "scanner efficient video analysis at scale"

Connected through hyperedge HE-5 (induced by source paper "surveiledge real time video query based on collaborative cloud edge deep learning"):
  with members:
    - "blazeit optimizing declarative aggregation and limit queries for neural network based video analytics"
    - "yolov3 an incremental improvement"
    - "blazeit fast exploratory video queries using neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "mask r cnn"
    - "tensorflow a system for large scale machine learning"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-6 (induced by source paper "evaluating temporal queries over video feeds"):
  with members:
    - "video monitoring queries"
    - "blazeit fast exploratory video queries using neural networks"
    - "focus querying large video datasets with low latency and low cost"
    - "simple online and realtime tracking with a deep association metric"
    - "mask r cnn"
    - "mot16 a benchmark for multi object tracking"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-7 (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-8 (induced by source paper "model slicing for supporting complex analytics with elastic inference cost and resource constraints"):
  with members:
    - "isbnet instance aware selective branching networks"
    - "group normalization"
    - "slimmable neural networks"
    - "skipnet learning dynamic routing in convolutional networks"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning efficient convolutional networks through network slimming"
    - "learning anytime predictions in neural networks via adaptive loss balancing"

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.DB(Databases)