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 "i safe instant suspicious activity identification at the edge using fuzzy decision making"):
  Abstract: Urban imagery usually serves as forensic analysis and by design is available for incident mitigation. As more imagery collected, it is harder to narrow down to certain frames among thousands of video clips to a specific incident. A real-time, proactive surveillance system is desirable, which could instantly detect dubious personnel, identify suspicious activities, or raise momentous alerts. The recent proliferation of the edge computing paradigm allows more data-intensive tasks to be accomplished by smart edge devices with lightweight but powerful algorithms. This paper presents a forensic surveillance strategy by introducing an Instant Suspicious Activity identiFication at the Edge (I-SAFE) using fuzzy decision making. A fuzzy control system is proposed to mimic the decision-making process of a security officer. Decisions are made based on video features extracted by a lightweight Deep Machine Learning (DML) model. Based on the requirements from the first-line law enforcement officers, several features are selected and fuzzified to cope with the state of uncertainty that exists in the officers' decision-making process. Using features in the edge hierarchy minimizes the communication delay such that instant alerting is achieved. Additionally, leveraging the Microservices architecture, the I-SAFE scheme possesses good scalability given the increasing complexities at the network edge. Implemented as an edge-based application and tested using exemplary and various labeled dataset surveillance videos, the I-SAFE scheme raises alerts by identifying the suspicious activity in an average of 0.002 seconds. Compared to four other state-of-the-art methods over two other data sets, the experimental study verified the superiority of the I-SAFE decentralized method.

Includes members:
  Member "decentralized smart surveillance through microservices platform":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "i vise interactive video surveillance as an edge service using unsupervised feature queries")
  Member "blendmas a blockchain enabled decentralized microservices architecture for smart public safety":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "decentralized smart surveillance through microservices platform")
  Member "vstore a data store for analytics on large videos":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "model assertions for monitoring and improving ml model")
  Member "real world anomaly detection in surveillance videos":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "centrality graph convolutional networks for skeleton based action recognition")
  Member "focus querying large video datasets with low latency and low cost":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "video monitoring queries")
  Member "kerman a hybrid lightweight tracking algorithm to enable smart surveillance as an edge service":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "i vise interactive video surveillance as an edge service using unsupervised feature queries")
  Member "real time index authentication for event oriented surveillance video query using blockchain":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "i vise interactive video surveillance as an edge service using unsupervised feature queries")
  Member "real time human detection as an edge service enabled by a lightweight cnn":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "decentralized smart surveillance through microservices platform")

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.CV(Computer Vision and Pattern Recognition)