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 "spacenet mvoi a multi view overhead imagery dataset"):
  Abstract: Detection and segmentation of objects in overheard imagery is a challenging task. The variable density, random orientation, small size, and instance-to-instance heterogeneity of objects in overhead imagery calls for approaches distinct from existing models designed for natural scene datasets. Though new overhead imagery datasets are being developed, they almost universally comprise a single view taken from directly overhead ("at nadir"), failing to address one critical variable: look angle. By contrast, views vary in real-world overhead imagery, particularly in dynamic scenarios such as natural disasters where first looks are often over 40 degrees off-nadir. This represents an important challenge to computer vision methods, as changing view angle adds distortions, alters resolution, and changes lighting. At present, the impact of these perturbations for algorithmic detection and segmentation of objects is untested. To address this problem, we introduce the SpaceNet Multi-View Overhead Imagery (MVOI) Dataset, an extension of the SpaceNet open source remote sensing dataset. MVOI comprises 27 unique looks from a broad range of viewing angles (-32 to 54 degrees). Each of these images cover the same geography and are annotated with 126,747 building footprint labels, enabling direct assessment of the impact of viewpoint perturbation on model performance. We benchmark multiple leading segmentation and object detection models on: (1) building detection, (2) generalization to unseen viewing angles and resolutions, and (3) sensitivity of building footprint extraction to changes in resolution. We find that segmentation and object detection models struggle to identify buildings in off-nadir imagery and generalize poorly to unseen views, presenting an important benchmark to explore the broadly relevant challenge of detecting small, heterogeneous target objects in visually dynamic contexts.

Includes members:
  Member "spacenet a remote sensing dataset and challenge series":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "road network and travel time extraction from multiple look angles with spacenet data")
  Member "xview objects in context in overhead imagery":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generating interpretable poverty maps using object detection in satellite images")
  Member "spatially transformed adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarial ranking attack and defense")
  Member "an occluded stacked hourglass approach to facial landmark localization and occlusion estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "lightweight real time makeup try on in mobile browsers with tiny cnn models for facial tracking")
  Member "the effects of super resolution on object detection performance in satellite imagery":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "longevity associated geometry identified in satellite images sidewalks driveways and hiking trails")
  Member "deepglobe 2018 a challenge to parse the earth through satellite images":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dense dilated convolutions merging network for land cover classification")
  Member "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ap mtl attention pruned multi task learning model for real time instrument detection and segmentation in robot assisted surgery")
  Member "you only look twice rapid multi scale object detection in satellite imagery":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep active learning for remote sensing object detection")

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)