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 "deep built structure counting in satellite imagery using attention based re weighting"):
  Abstract: In this paper, we attempt to address the challenging problem of counting built-structures in the satellite imagery. Building density is a more accurate estimate of the population density, urban area expansion and its impact on the environment, than the built-up area segmentation. However, building shape variances, overlapping boundaries, and variant densities make this a complex task. To tackle this difficult problem, we propose a deep learning based regression technique for counting built-structures in satellite imagery. Our proposed framework intelligently combines features from different regions of satellite image using attention based re-weighting techniques. Multiple parallel convolutional networks are designed to capture information at different granulates. These features are combined into the FusionNet which is trained to weigh features from different granularity differently, allowing us to predict a precise building count. To train and evaluate the proposed method, we put forward a new large-scale and challenging built-structure-count dataset. Our dataset is constructed by collecting satellite imagery from diverse geographical areas (planes, urban centers, deserts, etc.,) across the globe (Asia, Europe, North America, and Africa) and captures the wide density of built structures. Detailed experimental results and analysis validate the proposed technique. FusionNet has Mean Absolute Error of 3.65 and R-squared measure of 88% over the testing data. Finally, we perform the test on the 274:3 ? 103 m2 of the unseen region, with the error of 19 buildings off the 656 buildings in that area.

Includes members:
  Member "xview objects in context in overhead imagery":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generating interpretable poverty maps using object detection in satellite images")
  Member "exploring models and data for remote sensing image caption generation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discoverability in satellite imagery a good sentence is worth a thousand pictures")
  Member "patternnet a benchmark dataset for performance evaluation of remote sensing image retrieval":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bigearthnet dataset with a new class nomenclature for remote sensing image understanding")
  Member "detecting and counting tiny faces":
    also connected through other hyperedges:
      (none)
  Member "leveraging unlabeled data for crowd counting by learning to rank":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "deepglobe 2018 a challenge to parse the earth through satellite images":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dense dilated convolutions merging network for land cover classification")
  Member "switching convolutional neural network for crowd counting":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "decidenet counting varying density crowds through attention guided detection and density estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ha ccn hierarchical attention based crowd counting network")

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)