human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "finding interesting frames in deep video analytics a top k approach"):
  Abstract: Recently, the impressive accuracy of deep neural networks (DNNs) has created great demands on practical analytics over video data. Although efficient and accurate, the latest video analytics systems have not supported analytics beyond selection and aggregation yet. In data analytics, Top-K is a very important analytical operation that enables analysts to focus on the most important entities. In this paper, we present Everest, the first system that supports efficient and accurate Top-K video analytics. Everest can help rank and identify the most interesting frames/moments from videos with theoretical accuracy guarantees. Everest is built on a careful synthesis of machine learning, computer vision, and Top-K query processing. Evaluations on six real-world video streams and the latest Visual Road benchmark show that Everest can achieve between 10.8x to 17.9x higher efficiency than baseline approaches with high accuracy.

Includes members:
  Member "blazeit optimizing declarative aggregation and limit queries for neural network based video analytics":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "surveiledge real time video query based on collaborative cloud edge deep learning")
  Member "yolov3 an incremental improvement":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "focus querying large video datasets with low latency and low cost":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "video monitoring queries")
  Member "scanner efficient video analysis at scale":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximate query service on autonomous iot cameras")
  Member "occlusion aware hand pose estimation using hierarchical mixture density network":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an end to end framework for unsupervised pose estimation of occluded pedestrians")
  Member "face detection using deep learning an improved faster rcnn approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep learning for face recognition a critical analysis")
  Member "abnormality detection and localization in chest x rays using deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "justifying diagnosis decisions by deep neural networks")
  Member "noscope optimizing neural network queries over video at scale":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "video monitoring queries")

Question: Please tell me which class the source paper 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)