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: "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"
  Abstract: The trend towards higher resolution remote sensing imagery facilitates a transition from land-use classification to object-level scene understanding. Rather than relying purely on spectral content, appearance-based image features come into play. In this work, deep convolutional neural networks (CNNs) are applied to semantic labelling of high-resolution remote sensing data. Recent advances in fully convolutional networks (FCNs) are adapted to overhead data and shown to be as effective as in other domains. A full-resolution labelling is inferred using a deep FCN with no downsampling, obviating the need for deconvolution or interpolation. To make better use of image features, a pre-trained CNN is fine-tuned on remote sensing data in a hybrid network context, resulting in superior results compared to a network trained from scratch. The proposed approach is applied to the problem of labelling high-resolution aerial imagery, where fine boundary detail is important. The dense labelling yields state-of-the-art accuracy for the ISPRS Vaihingen and Potsdam benchmark data sets.

Connected through hyperedge HE-1 (induced by source paper "dense dilated convolutions merging network for land cover classification"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "dense fusion classmate network for land cover classification"
    - "on the convergence of adam and beyond"
    - "dense dilated convolutions merging network for semantic mapping of remote sensing images"
    - "sdc stacked dilated convolution a unified descriptor network for dense matching tasks"
    - "uncertainty gated network for land cover segmentation"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "urban land cover classification with missing data modalities using deep convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "hmanet hybrid multiple attention network for semantic segmentation in aerial images"):
  with members:
    - "interlaced sparse self attention for semantic segmentation"
    - "acfnet attentional class feature network for semantic segmentation"
    - "asymmetric non local neural networks for semantic segmentation"
    - "object contextual representations for semantic segmentation"
    - "eca net efficient channel attention for deep convolutional neural networks"
    - "a relation augmented fully convolutional network for semantic segmentation in aerial scenes"
    - "land cover mapping at very high resolution with rotation equivariant cnns towards small yet accurate models"
    - "bisenet bilateral segmentation network for real time semantic segmentation"

Connected through hyperedge HE-3 (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-4 (induced by source paper "a relation augmented fully convolutional network for semantic segmentation in aerial scenes"):
  with members:
    - "recurrently exploring class wise attention in a hybrid convolutional and bidirectional lstm network for multi label aerial image classification"
    - "land cover mapping at very high resolution with rotation equivariant cnns towards small yet accurate models"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "adaptive affinity fields for semantic segmentation"
    - "switchable temporal propagation network"
    - "relation networks for object detection"
    - "a simple neural network module for relational reasoning"
    - "learning affinity via spatial propagation networks"

Connected through hyperedge HE-5 (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-6 (induced by source paper "self attention for raw optical satellite time series classification"):
  with members:
    - "deep learning for time series classification a review"
    - "temporal convolutional neural network for the classification of satellite image time series"
    - "massively parallel hyperparameter tuning"
    - "the uea multivariate time series classification archive 2018"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "tune a research platform for distributed model selection and training"
    - "duplo a dual view point deep learning architecture for time series classification"
    - "the ucr time series archive"

Connected through hyperedge HE-7 (induced by source paper "dfpenet geology a deep learning framework for high precision recognition and segmentation of co seismic landslides"):
  with members:
    - "representation similarity analysis for efficient task taxonomy transfer learning"
    - "gff gated fully fusion for semantic segmentation"
    - "attention u net learning where to look for the pancreas"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "pyramid attention network for semantic segmentation"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "dilated residual networks"

Connected through hyperedge HE-8 (induced by source paper "dense dilated convolutions merging network for semantic mapping of remote sensing images"):
  with members:
    - "on the convergence of adam and beyond"
    - "urban land cover classification with missing data modalities using deep convolutional neural networks"
    - "rifcn recurrent network in fully convolutional network for semantic segmentation of high resolution remote sensing images"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "revisiting dilated convolution a simple approach for weakly and semi supervised semantic segmentation"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "joint learning from earth observation and openstreetmap data to get faster better semantic maps"
    - "understanding convolution for semantic segmentation"

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)