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: "blazeit fast exploratory video queries using neural networks"
  Abstract: As video volumes grow, analysts have increasingly turned to deep learning to process visual data. While these deep networks deliver impressive levels of accuracy, they execute as much as 10x slower than real time (3 fps) on a $8,000 GPU, which is infeasible at scale. In addition, deploying these methods requires writing complex, imperative code with many low-level libraries (e.g., OpenCV, MXNet), an often ad-hoc and time-consuming process that ignores opportunities for cross-operator optimization. To address the computational and usability challenges of video analytics at scale, we introduce BLAZEIT, a system that optimizes queries over video for spatiotemporal information of objects. BLAZEIT accepts queries via FRAMEQL, a declarative language for exploratory video analytics, that enables video-specific query optimization. We propose new query optimization techniques uniquely suited to video analytics that are not supported by prior work. First, we adapt control variates to video analytics and provide advances in specialization for aggregation queries. Second, we adapt importance-sampling using specialized NNs for cardinality-limited video search (i.e. scrubbing queries). Third, we show how to infer new classes of filters for content-based selection. By combining these optimizations, BLAZEIT can deliver over three order of magnitude speedups over the recent literature on video processing.

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"
    - "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"
    - "weakly supervised object localization with multi fold multiple instance learning"

Connected through hyperedge HE-2 (induced by source paper "robust extensible and fast teamed classifiers for vehicle tracking and vehicle re id in multi camera networks"):
  with members:
    - "boxcars improving fine grained recognition of vehicles using 3 d bounding boxes in traffic surveillance"
    - "towards universal object detection by domain attention"
    - "assed a framework for identifying physical events through adaptive social sensor data filtering"
    - "towards real time head pose estimation exploring parameter reduced residual networks on in the wild datasets"
    - "bag of tricks and a strong baseline for deep person re identification"
    - "cross domain knowledge learning with dual branch adversarial network for vehicle re identification"
    - "ram a region aware deep model for vehicle re identification"
    - "learning to compose topic aware mixture of experts for zero shot video captioning"

Connected through hyperedge HE-3 (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-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"
    - "scanner efficient video analysis at scale"
    - "rexcam resource efficient cross camera video analytics at enterprise 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"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "noscope optimizing neural network queries over video at scale"
    - "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"
    - "focus querying large video datasets with low latency and low cost"
    - "simple online and realtime tracking with a deep association metric"
    - "noscope optimizing neural network queries over video at scale"
    - "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 "robust extensible and fast teamed classifiers for vehicle tracking in multi camera networks"):
  with members:
    - "boxcars improving fine grained recognition of vehicles using 3 d bounding boxes in traffic surveillance"
    - "towards universal object detection by domain attention"
    - "assed a framework for identifying physical events through adaptive social sensor data filtering"
    - "vehicle re identification an efficient baseline using triplet embedding"
    - "domain adaptive attention model for unsupervised cross domain person re identification"
    - "towards real time head pose estimation exploring parameter reduced residual networks on in the wild datasets"
    - "do imagenet classifiers generalize to imagenet"
    - "bag of tricks and a strong baseline for deep person re identification"

Connected through hyperedge HE-8 (induced by source paper "rekall specifying video events using compositions of spatiotemporal labels"):
  with members:
    - "fast video shot transition localization with deep structured models"
    - "blazeit optimizing declarative aggregation and limit queries for neural network based video analytics"
    - "scanner efficient video analysis at scale"
    - "fine grained retrieval of sports plays using tree based alignment of trajectories"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "prototypical networks for few shot learning"
    - "inferring generative model structure with static analysis"
    - "snorkel rapid training data creation with weak supervision"

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)