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: "predicting economic development using geolocated wikipedia articles"
  Abstract: Progress on the UN Sustainable Development Goals (SDGs) is hampered by a persistent lack of data regarding key social, environmental, and economic indicators, particularly in developing countries. For example, data on poverty --- the first of seventeen SDGs --- is both spatially sparse and infrequently collected in Sub-Saharan Africa due to the high cost of surveys. Here we propose a novel method for estimating socioeconomic indicators using open-source, geolocated textual information from Wikipedia articles. We demonstrate that modern NLP techniques can be used to predict community-level asset wealth and education outcomes using nearby geolocated Wikipedia articles. When paired with nightlights satellite imagery, our method outperforms all previously published benchmarks for this prediction task, indicating the potential of Wikipedia to inform both research in the social sciences and future policy decisions.

Connected through hyperedge HE-1 (induced by source paper "learning when and where to zoom with deep reinforcement learning"):
  with members:
    - "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"
    - "learning to interpret satellite images in global scale using wikipedia"
    - "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:
    - "learning to interpret satellite images in global scale using wikipedia"
    - "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:
    - "reduced focal loss 1st place solution to xview object detection in satellite imagery"
    - "learning to interpret satellite images in global scale using wikipedia"
    - "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 "learning to interpret satellite images in global scale using wikipedia"):
  with members:
    - "learning to interpret satellite images using wikipedia"
    - "adversarial constraint learning for structured prediction"
    - "tile2vec unsupervised representation learning for spatially distributed data"
    - "exploring the limits of weakly supervised pretraining"
    - "learning two branch neural networks for image text matching tasks"
    - "snorkel rapid training data creation with weak supervision"
    - "aerial vehicle tracking by adaptive fusion of hyperspectral likelihood maps"
    - "learning aerial image segmentation from online maps"

Connected through hyperedge HE-5 (induced by source paper "cloud removal in satellite images using spatiotemporal generative networks"):
  with members:
    - "a remote sensing image dataset for cloud removal"
    - "learning to interpret satellite images in global scale using wikipedia"
    - "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"

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.LG(Machine Learning)