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: "urban land cover classification with missing data modalities using deep convolutional neural networks"
  Abstract: Automatic urban land cover classification is a fundamental problem in remote sensing, e.g., for environmental monitoring. The problem is highly challenging, as classes generally have high interclass and low intraclass variances. Techniques to improve urban land cover classification performance in remote sensing include fusion of data from different sensors with different data modalities. However, such techniques require all modalities to be available to the classifier in the decision-making process, i.e., at test time, as well as in training. If a data modality is missing at test time, current state-of-the-art approaches have in general no procedure available for exploiting information from these modalities. This represents a waste of potentially useful information. We propose as a remedy a convolutional neural network (CNN) architecture for urban land cover classification which is able to embed all available training modalities in the so-called hallucination network. The network will in effect replace missing data modalities in the test phase, enabling fusion capabilities even when data modalities are missing in testing. We demonstrate the method using two datasets consisting of optical and digital surface model (DSM) images. We simulate missing modalities by assuming that DSM images are missing during testing. Our method outperforms both standard CNNs trained only on optical images as well as an ensemble of two standard CNNs. We further evaluate the potential of our method to handle situations where only some DSM images are missing during testing. Overall, we show that we can clearly exploit training time information of the missing modality during testing.

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"
    - "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 "dense dilated convolutions merging network for semantic mapping of remote sensing images"):
  with members:
    - "on the convergence of adam and beyond"
    - "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"
    - "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"

Connected through hyperedge HE-3 (induced by source paper "human machine collaboration for fast land cover mapping"):
  with members:
    - "dense fusion classmate network for land cover classification"
    - "group normalization"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "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-4 (induced by source paper "online sensor hallucination via knowledge distillation for multimodal image classification"):
  with members:
    - "modality distillation with multiple stream networks for action recognition"
    - "graph distillation for action detection with privileged modalities"
    - "beyond rgb very high resolution urban remote sensing with multimodal deep networks"
    - "unsupervised deep feature extraction for remote sensing image classification"
    - "cross modal distillation for supervision transfer"
    - "two stream convolutional networks for action recognition in videos"
    - "advances in hyperspectral image classification earth monitoring with statistical learning methods"

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)