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: "comprehensive survey of deep learning in remote sensing theories tools and challenges for the community"
  Abstract: In recent years, deep learning (DL), a rebranding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, and natural language processing. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV, e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should not only be aware of advancements such as DL, but also be leading researchers in this area. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools, and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as they relate to (i) inadequate data sets, (ii) human-understandable solutions for modeling physical phenomena, (iii) big data, (iv) nontraditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial, and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.

Connected through hyperedge HE-1 (induced by source paper "generalizing deep models for overhead image segmentation through getis ord gi pooling"):
  with members:
    - "a relation augmented fully convolutional network for semantic segmentation in aerial scenes"
    - "domain adaptation for structured output via discriminative patch representations"
    - "learning to adapt structured output space for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "improving semantic segmentation via video propagation and label relaxation"
    - "semantic labeling in very high resolution images via a self cascaded convolutional neural network"
    - "no more discrimination cross city adaptation of road scene segmenters"
    - "cycada cycle consistent adversarial domain adaptation"

Connected through hyperedge HE-2 (induced by source paper "in domain representation learning for remote sensing"):
  with members:
    - "the visual task adaptation benchmark"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "do better imagenet models transfer better"
    - "domain adaptive transfer learning with specialist models"
    - "exploring the limits of weakly supervised pretraining"
    - "marta gans unsupervised representation learning for remote sensing image classification"
    - "filmy cloud removal on satellite imagery with multispectral conditional generative adversarial nets"
    - "remote sensing image scene classification benchmark and state of the art"

Connected through hyperedge HE-3 (induced by source paper "semantic referee a neural symbolic framework for enhancing geospatial semantic segmentation"):
  with members:
    - "location augmentation for cnn"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "deep learning in remote sensing a review"
    - "explaining trained neural networks with semantic web technologies first steps"
    - "neural symbolic learning and reasoning a survey and interpretation"
    - "relating input concepts to convolutional neural network decisions"
    - "what does explainable ai really mean a new conceptualization of perspectives"
    - "how a general purpose commonsense ontology can improve performance of learning based image retrieval"

Connected through hyperedge HE-4 (induced by source paper "a comparison and strategy of semantic segmentation on remote sensing images"):
  with members:
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "multi task learning for segmentation of building footprints with deep neural networks"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "pyramid scene parsing network"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-5 (induced by source paper "state of the art and gaps for deep learning on limited training data in remote sensing"):
  with members:
    - "generative adversarial networks and probabilistic graph models for hyperspectral image classification"
    - "image to image translation with conditional adversarial networks"
    - "deep plant plant identification with convolutional neural networks"
    - "transfer learning from deep features for remote sensing and poverty mapping"
    - "return of the devil in the details delving deep into convolutional nets"
    - "imagenet large scale visual recognition challenge"
    - "marginalized denoising autoencoders for domain adaptation"

Connected through hyperedge HE-6 (induced by source paper "multisource and multitemporal data fusion in remote sensing"):
  with members:
    - "effective sequential classifier training for svm based multitemporal remote sensing image classification"
    - "toward seamless multiview scene analysis from satellite to street level"
    - "beyond rgb very high resolution urban remote sensing with multimodal deep networks"
    - "a convex formulation for hyperspectral image superresolution via subspace based regularization"
    - "very deep convolutional networks for large scale image recognition"
    - "hyperspectral and multispectral image fusion based on a sparse representation"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "a review on the application of natural computing in environmental informatics"):
  with members:
    - "deep learning in agriculture a survey"
    - "disaster monitoring using unmanned aerial vehicles and deep learning"
    - "crop planning using stochastic visual optimization"

Connected through hyperedge HE-8 (induced by source paper "deep learning hyperspectral image classification using multiple class based denoising autoencoders mixed pixel training augmentation and morphological operations"):
  with members:
    - "marginalized denoising autoencoders for domain adaptation"

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)