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 optimizing declarative aggregation and limit queries for neural network based video analytics"
  Abstract: Recent advances in neural networks (NNs) have enabled automatic querying of large volumes of video data with high accuracy. While these deep NNs can produce accurate annotations of an object's position and type in video, they are computationally expensive and require complex, imperative deployment code to answer queries. Prior work uses approximate filtering to reduce the cost of video analytics, but does not handle two important classes of queries, aggregation and limit queries; moreover, these approaches still require complex code to deploy. To address the computational and usability challenges of querying video at scale, we introduce BlazeIt, a system that optimizes queries of spatiotemporal information of objects in video. BlazeIt accepts queries via FrameQL, a declarative extension of SQL for video analytics that enables video-specific query optimization. We introduce two new query optimization techniques in BlazeIt that are not supported by prior work. First, we develop methods of using NNs as control variates to quickly answer approximate aggregation queries with error bounds. Second, we present a novel search algorithm for cardinality-limited video queries. Through these these optimizations, BlazeIt can deliver up to 83x speedups over the recent literature on video processing.

Connected through hyperedge HE-1 (induced by source paper "finding interesting frames in deep video analytics a top k approach"):
  with members:
    - "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"
    - "noscope optimizing neural network queries over video at scale"
    - "mask r cnn"

Connected through hyperedge HE-2 (induced by source paper "surveiledge real time video query based on collaborative cloud edge deep learning"):
  with members:
    - "yolov3 an incremental improvement"
    - "blazeit fast exploratory video queries using neural networks"
    - "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-3 (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-4 (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 fast exploratory video queries using neural networks"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "physical representation based predicate optimization for a visual analytics database"):
  with members:
    - "blazeit fast exploratory video queries using neural networks"
    - "verdictdb universalizing approximate query processing"
    - "focus querying large video datasets with low latency and low cost"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeeze and excitation networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "noscope optimizing neural network queries over video at scale"
    - "macrobase prioritizing attention in fast data"

Connected through hyperedge HE-6 (induced by source paper "supporting video queries on zero streaming cameras"):
  with members:
    - "scaling video analytics on constrained edge nodes"
    - "vstore a data store for analytics on large videos"
    - "secs efficient deep stream processing via class skew dichotomy"
    - "yolov3 an incremental improvement"
    - "focus querying large video datasets with low latency and low cost"
    - "deeplens towards a visual data management system"
    - "rexcam resource efficient cross camera video analytics at enterprise scale"
    - "clickbait v2 training an object detector in real time"

Connected through hyperedge HE-7 (induced by source paper "deeplens towards a visual data management system"):
  with members:
    - "physical representation based predicate optimization for a visual analytics database"
    - "the case for automatic database administration using deep reinforcement learning"
    - "learning to optimize join queries with deep reinforcement learning"
    - "cuttlefish a lightweight primitive for adaptive query processing"
    - "smoke fine grained lineage at interactive speed"
    - "noscope optimizing neural network queries over video at scale"
    - "deeper depth prediction with fully convolutional residual networks"
    - "cudnn efficient primitives for deep learning"

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)