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: "delugenets deep networks with efficient and flexible cross layer information inflows"
  Abstract: Deluge Networks (DelugeNets) are deep neural networks which efficiently facilitate massive cross-layer information inflows from preceding layers to succeeding layers. The connections between layers in DelugeNets are established through cross-layer depthwise convolutional layers with learnable filters, acting as a flexible yet efficient selection mechanism. DelugeNets can propagate information across many layers with greater flexibility and utilize network parameters more effectively compared to ResNets, whilst being more efficient than DenseNets. Remarkably, a DelugeNet model with just model complexity of 4.31 GigaFLOPs and 20.2M network parameters, achieve classification errors of 3.76% and 19.02% on CIFAR-10 and CIFAR-100 dataset respectively. Moreover, DelugeNet-122 performs competitively to ResNet-200 on ImageNet dataset, despite costing merely half of the computations needed by the latter.

Connected through hyperedge HE-1 (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-2 (induced by source paper "convolutional neural networks combined with runge kutta methods"):
  with members:
    - "convolutional neural networks with alternately updated clique"
    - "deep learning with data dependent implicit activation function"
    - "neural ordinary differential equations"
    - "learning across scales a multiscale method for convolution neural networks"
    - "physics informed deep learning part ii data driven discovery of nonlinear partial differential equations"
    - "memory efficient implementation of densenets"
    - "maximum principle based algorithms for deep learning"
    - "beyond finite layer neural networks bridging deep architectures and numerical differential equations"

Connected through hyperedge HE-3 (induced by source paper "non linear convolution filters for cnn based learning"):
  with members:
    - "oriented response networks"
    - "aggregated residual transformations for deep neural networks"
    - "deep networks with stochastic depth"
    - "identity mappings in deep residual networks"
    - "wide residual networks"
    - "deep pyramidal residual networks"
    - "regularizing cnns with locally constrained decorrelations"
    - "deep residual learning for image recognition"

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)