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: "rapid building detection using machine learning"
  Abstract: This work describes algorithms for performing discrete object detection, specifically in the case of buildings, where usually only low quality RGB-only geospatial reflective imagery is available. We utilize new candidate search and feature extraction techniques to reduce the problem to a machine learning (ML) classification task. Here we can harness the complex patterns of contrast features contained in training data to establish a model of buildings. We avoid costly sliding windows to generate candidates; instead we innovatively stitch together well known image processing techniques to produce candidates for building detection that cover 80-85% of buildings. Reducing the number of possible candidates is important due to the scale of the problem. Each candidate is subjected to classification which, although linear, costs time and prohibits large scale evaluation. We propose a candidate alignment algorithm to boost classification performance to 80-90% precision with a linear time algorithm and show it has negligible cost. Also, we propose a new concept called a Permutable Haar Mesh (PHM) which we use to form and traverse a search space to recover candidate buildings which were lost in the initial preprocessing phase.

Connected through hyperedge HE-1 (induced by source paper "learning orientation estimation convolutional neural network for building detection in optical remote sensing image"):
  with members:
    - "arbitrary oriented scene text detection via rotation proposals"
    - "r2cnn rotational region cnn for orientation robust scene text detection"
    - "mask r cnn"
    - "a survey on object detection in optical remote sensing images"
    - "vehicle detection from 3d lidar using fully convolutional network"
    - "ssd single shot multibox detector"
    - "you only look once unified real time object detection"
    - "fast r cnn"

Connected through hyperedge HE-2 (induced by source paper "environment aware deployment of wireless drones base stations with google earth simulator"):
  with members:
    - "beyond 5g with uavs foundations of a 3d wireless cellular network"
    - "on the number and 3d placement of drone base stations in wireless cellular networks"
    - "learning to communicate in uav aided wireless networks map based approaches"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "wireless communication using unmanned aerial vehicles uavs optimal transport theory for hover time optimization"
    - "joint trajectory and communication design for uav enabled multiple access"
    - "building detection from satellite images on a global scale"
    - "efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage"

Connected through hyperedge HE-3 (induced by source paper "automatic prediction of building age from photographs"):
  with members:
    - "a survey on object detection in optical remote sensing images"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "matconvnet convolutional neural networks for matlab"

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)