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: "dense fusion classmate network for land cover classification"
  Abstract: Recently, FCNs based methods have made great progress in semantic segmentation. Different with ordinary scenes, satellite image owns specific characteristics, which elements always extend to large scope and no regular or clear boundaries. Therefore, effective mid-level structure information extremely missing, precise pixel-level classification becomes tough issues. In this paper, a Dense Fusion Classmate Network (DFCNet) is proposed to adopt in land cover classification.

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"
    - "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"
    - "rifcn recurrent network in fully convolutional network for semantic segmentation of high resolution remote sensing images"

Connected through hyperedge HE-2 (induced by source paper "rsvqa visual question answering for remote sensing data"):
  with members:
    - "correcting rural building annotations in openstreetmap using convolutional neural networks"
    - "r 3 net a deep network for multioriented vehicle detection in aerial images and videos"
    - "answer them all toward universal visual question answering models"
    - "learning to count objects in natural images for visual question answering"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "mutan multimodal tucker fusion for visual question answering"
    - "dota a large scale dataset for object detection in aerial images"

Connected through hyperedge HE-3 (induced by source paper "a comprehensive analysis of weakly supervised semantic segmentation in different image domains"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "weakly supervised learning of instance segmentation with inter pixel relations"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "constrained cnn losses for weakly supervised segmentation"
    - "weakly supervised semantic segmentation of satellite images"
    - "weakly supervised instance segmentation using class peak response"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"

Connected through hyperedge HE-4 (induced by source paper "dv3 hed a dcnns based framework to monitor temporary works and esas in railway construction project using vhr satellite images"):
  with members:
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "linknet exploiting encoder representations for efficient semantic segmentation"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "high resolution aerial image labeling with convolutional neural networks"
    - "rethinking atrous convolution for semantic image segmentation"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-5 (induced by source paper "improving land cover segmentation across satellites using domain adaptation"):
  with members:
    - "all about structure adapting structural information across domains for boosting semantic segmentation"
    - "bidirectional learning for domain adaptation of semantic segmentation"
    - "learning to adapt structured output space for semantic segmentation"
    - "deep visual domain adaptation a survey"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "advent adversarial entropy minimization for domain adaptation in semantic segmentation"
    - "no more discrimination cross city adaptation of road scene segmenters"

Connected through hyperedge HE-6 (induced by source paper "adaptive compression based lifelong learning"):
  with members:
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "packnet adding multiple tasks to a single network by iterative pruning"
    - "encoder based lifelong learning"
    - "overcoming catastrophic forgetting by incremental moment matching"
    - "overcoming catastrophic forgetting in neural networks"
    - "learning without forgetting"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "human machine collaboration for fast land cover mapping"):
  with members:
    - "group normalization"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "urban land cover classification with missing data modalities using deep convolutional neural networks"
    - "prototypical networks for few shot learning"
    - "machine teaching a new paradigm for building machine learning systems"
    - "film visual reasoning with a general conditioning layer"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "matching networks for one shot learning"

Connected through hyperedge HE-8 (induced by source paper "arcticnet a deep learning solution to classify arctic wetlands"):
  with members:
    - "collaborative global local networks for memory efficient segmentation of ultra high resolution images"
    - "focal loss for dense object detection"
    - "joint learning from earth observation and openstreetmap data to get faster better semantic maps"
    - "when image denoising meets high level vision tasks a deep learning approach"
    - "beyond rgb very high resolution urban remote sensing with multimodal deep networks"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "deep residual learning for image recognition"
    - "fully convolutional networks 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)