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: "learning to interpret satellite images in global scale using wikipedia"
  Abstract: Despite recent progress in computer vision, finegrained interpretation of satellite images remains challenging because of a lack of labeled training data. To overcome this limitation, we construct a novel dataset called WikiSatNet by pairing georeferenced Wikipedia articles with satellite imagery of their corresponding locations. We then propose two strategies to learn representations of satellite images by predicting properties of the corresponding articles from the images. Leveraging this new multi-modal dataset, we can drastically reduce the quantity of human-annotated labels and time required for downstream tasks. On the recently released fMoW dataset, our pre-training strategies can boost the performance of a model pre-trained on ImageNet by up to 4:5% in F1 score.

Connected through hyperedge HE-1 (induced by source paper "learning when and where to zoom with deep reinforcement learning"):
  with members:
    - "predicting economic development using geolocated wikipedia articles"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "on low resolution face recognition in the wild comparisons and new techniques"
    - "efficient object detection in large images using deep reinforcement learning"
    - "learning to interpret satellite images using wikipedia"
    - "dropblock a regularization method for convolutional networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "resource aware person re identification across multiple resolutions"

Connected through hyperedge HE-2 (induced by source paper "generating interpretable poverty maps using object detection in satellite images"):
  with members:
    - "predicting economic development using geolocated wikipedia articles"
    - "efficient object detection in large images using deep reinforcement learning"
    - "xview objects in context in overhead imagery"
    - "yolov3 an incremental improvement"
    - "consistent individualized feature attribution for tree ensembles"
    - "focal loss for dense object detection"
    - "dssd deconvolutional single shot detector"
    - "a unified approach to interpreting model predictions"

Connected through hyperedge HE-3 (induced by source paper "efficient object detection in large images using deep reinforcement learning"):
  with members:
    - "predicting economic development using geolocated wikipedia articles"
    - "reduced focal loss 1st place solution to xview object detection in satellite imagery"
    - "using pre training can improve model robustness and uncertainty"
    - "learning to interpret satellite images using wikipedia"
    - "xview objects in context in overhead imagery"
    - "autofocus efficient multi scale inference"
    - "yolov3 an incremental improvement"
    - "enkcf ensemble of kernelized correlation filters for high speed object tracking"

Connected through hyperedge HE-4 (induced by source paper "cloud removal in satellite images using spatiotemporal generative networks"):
  with members:
    - "predicting economic development using geolocated wikipedia articles"
    - "a remote sensing image dataset for cloud removal"
    - "attentive generative adversarial network for raindrop removal from a single image"
    - "aerial vehicle tracking by adaptive fusion of hyperspectral likelihood maps"
    - "filmy cloud removal on satellite imagery with multispectral conditional generative adversarial nets"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "toward multimodal image to image translation"
    - "image de raining using a conditional generative adversarial network"

Connected through hyperedge HE-5 (induced by source paper "predicting economic development using geolocated wikipedia articles"):
  with members:
    - "learning to interpret satellite images using wikipedia"
    - "infrastructure quality assessment in africa using satellite imagery and deep learning"
    - "learning two branch neural networks for image text matching tasks"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "breakingnews article annotation by image and text processing"
    - "text matching as image recognition"
    - "predicting deep zero shot convolutional neural networks using textual descriptions"
    - "transfer learning from deep features for remote sensing and poverty mapping"

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)