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: "bigearthnet a large scale benchmark archive for remote sensing image understanding"
  Abstract: This paper presents the BigEarthNet that is a new large-scale multi-label Sentinel-2 benchmark archive. The BigEarthNet consists of 590, 326 Sentinel-2 image patches, each of which is a section of i) 120 × 120 pixels for 10m bands; ii) 60×60 pixels for 20m bands; and iii) 20×20 pixels for 60m bands. Unlike most of the existing archives, each image patch is annotated by multiple land-cover classes (i.e., multi-labels) that are provided from the CORINE Land Cover database of the year 2018 (CLC 2018). The BigEarthNet is significantly larger than the existing archives in remote sensing (RS) and thus is much more convenient to be used as a training source in the context of deep learning. This paper first addresses the limitations of the existing archives and then describes the properties of the BigEarthNet. Experimental results obtained in the framework of RS image scene classification problems show that a shallow Convolutional Neural Network (CNN) architecture trained on the BigEarthNet provides much higher accuracy compared to a state-of-the-art CNN model pre-trained on the ImageNet (which is a very popular large-scale benchmark archive in computer vision). The BigEarthNet opens up promising directions to advance operational RS applications and research in massive Sentinel-2 image archives.

Connected through hyperedge HE-1 (induced by source paper "a framework for large scale mapping of human settlement extent from sentinel 2 images via fully convolutional neural networks"):
  with members:
    - "sen12ms a curated dataset of georeferenced multi spectral sentinel 1 2 imagery for deep learning and data fusion"
    - "relation network for multi label aerial image classification"
    - "so2sat lcz42 a benchmark dataset for global local climate zones classification"
    - "recurrently exploring class wise attention in a hybrid convolutional and bidirectional lstm network for multi label aerial image classification"
    - "mapping missing population in rural india a deep learning approach with satellite imagery"
    - "learnable manifold alignment lema a semi supervised cross modality learning framework for land cover and land use classification"
    - "cospace common subspace learning from hyperspectral multispectral correspondences"
    - "an augmented linear mixing model to address spectral variability for hyperspectral unmixing"

Connected through hyperedge HE-2 (induced by source paper "bigearthnet dataset with a new class nomenclature for remote sensing image understanding"):
  with members:
    - "a novel multi attention driven system for multi label remote sensing image classification"
    - "recurrently exploring class wise attention in a hybrid convolutional and bidirectional lstm network for multi label aerial image classification"
    - "patternnet a benchmark dataset for performance evaluation of remote sensing image retrieval"
    - "building instance classification using street view images"
    - "towards multi class object detection in unconstrained remote sensing imagery"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "beyond rgb very high resolution urban remote sensing with multimodal deep networks"
    - "remote sensing image scene classification benchmark and state of the art"

Connected through hyperedge HE-3 (induced by source paper "bigearthnet deep learning models with a new class nomenclature for remote sensing image understanding"):
  with members:
    - "patternnet a benchmark dataset for performance evaluation of remote sensing image retrieval"
    - "building instance classification using street view images"
    - "towards multi class object detection in unconstrained remote sensing imagery"
    - "beyond rgb very high resolution urban remote sensing with multimodal deep networks"
    - "deep residual learning for image recognition"
    - "transfer learning from deep features for remote sensing and poverty mapping"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "segmentation of satellite imagery using u net models for land cover classification"):
  with members:
    - "fastai a layered api for deep learning"
    - "object based multi temporal and multi source land cover mapping leveraging hierarchical class relationships"
    - "weakly supervised semantic segmentation of satellite images"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "a survey on deep transfer learning"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-5 (induced by source paper "spotting insects from satellites modeling the presence of culicoides imicola through deep cnns"):
  with members:
    - "classifying signals on irregular domains via convolutional cluster pooling"
    - "attention based deep multiple instance learning"
    - "deep sets"
    - "deep learning approach for building detection in satellite multispectral imagery"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "semi supervised classification with graph convolutional networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-6 (induced by source paper "sen12ms a curated dataset of georeferenced multi spectral sentinel 1 2 imagery for deep learning and data fusion"):
  with members:
    - "cloud net an end to end cloud detection algorithm for landsat 8 imagery"
    - "the sen1 2 dataset for deep learning in sar optical data fusion"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "dota a large scale dataset for object detection in aerial images"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "deep residual learning for image recognition"
    - "deepsat a learning framework for satellite imagery"

Connected through hyperedge HE-7 (induced by source paper "a novel multi attention driven system for multi label remote sensing image classification"):
  with members:
    - "recurrently exploring class wise attention in a hybrid convolutional and bidirectional lstm network for multi label aerial image classification"
    - "remote sensing image scene classification benchmark and state of the art"

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)