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: "industrial smoke detection and visualization"
  Abstract: As sensing technology proliferates and becomes affordable to the general public, there is a growing trend in citizen science where scientists and volunteers form a strong partnership in conducting scientific research including problem finding, data collection, analysis, visualization, and storytelling. Providing easy-to-use computational tools to support citizen science has become an important issue. To raise the public awareness of environmental science and improve the air quality in local areas, we are currently collaborating with a local community in monitoring and documenting fugitive emissions from a coke refinery. We have helped the community members build a live camera system which captures and visualizes high resolution timelapse imagery starting from November 2014. However, searching and documenting smoke emissions manually from all video frames requires manpower and takes an impractical investment of time. This paper describes a software tool which integrates four features: (1) an algorithm based on change detection and texture segmentation for identifying smoke emissions; (2) an interactive timeline visualization providing indicators for seeking to interesting events; (3) an autonomous fast-forwarding mode for skipping uninteresting timelapse frames; and (4) a collection of animated smoke images generated automatically according to the algorithm for documentation, presentation, storytelling, and sharing. With the help of this tool, citizen scientists can now focus on the content of the story instead of time-consuming and laborious works.

Connected through hyperedge HE-1 (induced by source paper "machine vision for natural gas methane emissions detection using an infrared camera"):
  with members:
    - "an overview of gradient descent optimization algorithms"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "image segmentation in video sequences a probabilistic approach"
    - "multi column deep neural networks for image classification"
    - "improving neural networks by preventing co adaptation of feature detectors"

Connected through hyperedge HE-2 (induced by source paper "smell pittsburgh engaging community citizen science for air quality"):
  with members:
    - "when human computer interaction meets community citizen science"
    - "community empowered air quality monitoring system"
    - "why should i trust you explaining the predictions of any classifier"
    - "the emotional and chromatic layers of urban smells"
    - "smelly maps the digital life of urban smellscapes"

Connected through hyperedge HE-3 (induced by source paper "smell pittsburgh community empowered mobile smell reporting system"):
  with members:
    - "community empowered air quality monitoring system"
    - "why should i trust you explaining the predictions of any classifier"
    - "the emotional and chromatic layers of urban smells"
    - "smelly maps the digital life of urban smellscapes"

Connected through hyperedge HE-4 (induced by source paper "visualization tool for environmental sensing and public health data"):
  with members:
    - "community empowered air quality monitoring system"

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.CV(Computer Vision and Pattern Recognition)