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: "transfer learning from deep features for remote sensing and poverty mapping"
  Abstract: The lack of reliable data in developing countries is a major obstacle to sustainable development, food security, and disaster relief. Poverty data, for example, is typically scarce, sparse in coverage, and labor-intensive to obtain. Remote sensing data such as high-resolution satellite imagery, on the other hand, is becoming increasingly available and inexpensive. Unfortunately, such data is highly unstructured and currently no techniques exist to automatically extract useful insights to inform policy decisions and help direct humanitarian efforts. We propose a novel machine learning approach to extract large-scale socioeconomic indicators from high-resolution satellite imagery. The main challenge is that training data is very scarce, making it difficult to apply modern techniques such as Convolutional Neural Networks (CNN). We therefore propose a transfer learning approach where nighttime light intensities are used as a data-rich proxy. We train a fully convolutional CNN model to predict nighttime lights from daytime imagery, simultaneously learning features that are useful for poverty prediction. The model learns filters identifying different terrains and man-made structures, including roads, buildings, and farmlands, without any supervision beyond nighttime lights. We demonstrate that these learned features are highly informative for poverty mapping, even approaching the predictive performance of survey data collected in the field.

Connected through hyperedge HE-1 (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"
    - "bigearthnet a large scale benchmark archive for remote sensing image understanding"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "autofcl automatically tuning fully connected layers for transfer learning"):
  with members:
    - "impact of fully connected layers on performance of convolutional neural networks for image classification"
    - "progressive neural architecture search"
    - "learning transferable architectures for scalable image recognition"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

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:
    - "bigearthnet a large scale benchmark archive for remote sensing image understanding"
    - "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"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "transfer adaptation learning a decade survey"):
  with members:
    - "optimal projection guided transfer hashing for image retrieval"
    - "representation learning by rotating your faces"
    - "open set domain adaptation for image and action recognition"
    - "deep visual domain adaptation a survey"
    - "domain adaptive faster r cnn for object detection in the wild"
    - "load balanced gans for multi view face image synthesis"
    - "learning domain invariant subspace using domain features and independence maximization"
    - "importance weighted adversarial nets for partial domain adaptation"

Connected through hyperedge HE-5 (induced by source paper "resunet a a deep learning framework for semantic segmentation of remotely sensed data"):
  with members:
    - "rethinking imagenet pre training"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "road extraction by deep residual u net"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "context encoding for semantic segmentation"
    - "semantic labeling in very high resolution images via a self cascaded convolutional neural network"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "adversarial transfer learning for cross domain visual recognition"):
  with members:
    - "when unsupervised domain adaptation meets tensor representations"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "generate to adapt aligning domains using generative adversarial networks"
    - "dualgan unsupervised dual learning for image to image translation"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"
    - "improved techniques for training gans"
    - "deep transfer learning with joint adaptation networks"

Connected through hyperedge HE-7 (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"
    - "comprehensive survey of deep learning in remote sensing theories tools and challenges for the community"
    - "filmy cloud removal on satellite imagery with multispectral conditional generative adversarial nets"

Connected through hyperedge HE-8 (induced by source paper "effective transfer learning for hyperspectral image classification with deep convolutional neural networks"):
  with members:
    - "going deeper with contextual cnn for hyperspectral image classification"
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "gate variants of gated recurrent unit gru neural networks"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "identity mappings in deep residual networks"
    - "deep residual learning for image recognition"
    - "how transferable are features in deep neural networks"
    - "network in 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.CV(Computer Vision and Pattern Recognition)