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: "depth cnns for rgb d scene recognition learning from scratch better than transferring from rgb cnns"
  Abstract: Scene recognition with RGB images has been extensively studied and has reached very remarkable recognition levels, thanks to convolutional neural networks (CNN) and large scene datasets. In contrast, current RGB-D scene data is much more limited, so often leverages RGB large datasets, by transferring pretrained RGB CNN models and fine-tuning with the target RGB-D dataset. However, we show that this approach has the limitation of hardly reaching bottom layers, which is key to learn modality-specific features. In contrast, we focus on the bottom layers, and propose an alternative strategy to learn depth features combining local weakly supervised training from patches followed by global fine tuning with images. This strategy is capable of learning very discriminative depth-specific features with limited depth images, without resorting to Places-CNN. In addition we propose a modified CNN architecture to further match the complexity of the model and the amount of data available. For RGB-D scene recognition, depth and RGB features are combined by projecting them in a common space and further leaning a multilayer classifier, which is jointly optimized in an end-to-end network. Our framework achieves state-of-the-art accuracy on NYU2 and SUN RGB-D in both depth only and combined RGB-D data.

Connected through hyperedge HE-1 (induced by source paper "mix and match networks multi domain alignment for unpaired image to image translation"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "learning to adapt structured output space for semantic segmentation"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "mix and match networks encoder decoder alignment for zero pair image translation"
    - "depth aware cnn for rgb d segmentation"
    - "multimodal unsupervised image to image translation"
    - "conditional image to image translation"
    - "image to image translation for cross domain disentanglement"

Connected through hyperedge HE-2 (induced by source paper "deep multi view learning using neuron wise correlation maximizing regularizers"):
  with members:
    - "dominant set clustering and pooling for multi view 3d object recognition"
    - "multi view intact space learning"
    - "theory of deep learning iib optimization properties of sgd"
    - "data dependent stability of stochastic gradient descent"
    - "aggregated residual transformations for deep neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "identity mappings in deep residual networks"
    - "rotationnet joint object categorization and pose estimation using multiviews from unsupervised viewpoints"

Connected through hyperedge HE-3 (induced by source paper "synthetic data generation for end to end thermal infrared tracking"):
  with members:
    - "meta tracker fast and robust online adaptation for visual object trackers"
    - "good features to correlate for visual tracking"
    - "dcfnet discriminant correlation filters network for visual tracking"
    - "end to end representation learning for correlation filter based tracking"
    - "learning cross modal deep representations for robust pedestrian detection"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "crest convolutional residual learning for visual tracking"
    - "learning background aware correlation filters for visual tracking"

Connected through hyperedge HE-4 (induced by source paper "the rgb d triathlon towards agile visual toolboxes for robots"):
  with members:
    - "the limits and potentials of deep learning for robotics"
    - "adding new tasks to a single network with weight transformations using binary masks"
    - "taskonomy disentangling task transfer learning"
    - "rgb d object detection and semantic segmentation for autonomous manipulation in clutter"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "3d pose regression using convolutional neural networks"
    - "packnet adding multiple tasks to a single network by iterative pruning"
    - "analyzing modular cnn architectures for joint depth prediction and semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "rfbnet deep multimodal networks with residual fusion blocks for rgb d semantic segmentation"):
  with members:
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "restricted deformable convolution based road scene semantic segmentation using surround view cameras"
    - "gff gated fully fusion for semantic segmentation"
    - "3dmv joint 3d multi view prediction for 3d semantic scene segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "long term visual localization using semantically segmented images"
    - "in defense of classical image processing fast depth completion on the cpu"
    - "rednet residual encoder decoder network for indoor rgb d semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "learning effective rgb d representations for scene recognition"):
  with members:
    - "weakly supervised patchnets describing and aggregating local patches for scene recognition"
    - "correlated and individual multi modal deep learning for rgb d object recognition"
    - "convolutional two stream network fusion for video action recognition"
    - "weakly supervised deep detection networks"
    - "deep residual learning for image recognition"
    - "cross modal distillation for supervision transfer"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "multimodal deep learning for robust rgb d object recognition"

Connected through hyperedge HE-7 (induced by source paper "unsupervised domain adaptation learning algorithm for rgb d staircase recognition"):
  with members:
    - "bag of tricks for image classification with convolutional neural networks"
    - "megadepth learning single view depth prediction from internet photos"
    - "estimated depth map helps image classification"
    - "learning discriminative features via label consistent neural network"
    - "simultaneous deep transfer across domains and tasks"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "revisiting single image depth estimation toward higher resolution maps with accurate object boundaries"):
  with members:
    - "deep ordinal regression network for monocular depth estimation"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "rednet residual encoder decoder network for indoor rgb d semantic segmentation"
    - "joint prediction of depths normals and surface curvature from rgb images using cnns"
    - "squeeze and excitation networks"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "demon depth and motion network for learning monocular stereo"
    - "joint estimation of camera pose depth deblurring and super resolution from a blurred image sequence"

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)