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: "a deep representation for depth images from synthetic data"
  Abstract: Convolutional Neural Networks (CNNs) trained on large scale RGB databases have become the secret sauce in the majority of recent approaches for object categorization from RGB-D data. Thanks to colorization techniques, these methods exploit the filters learned from 2D images to extract meaningful representations in 2.5D. Still, the perceptual signature of these two kind of images is very different, with the first usually strongly characterized by textures, and the second mostly by silhouettes of objects. Ideally, one would like to have two CNNs, one for RGB and one for depth, each trained on a suitable data collection, able to capture the perceptual properties of each channel for the task at hand. This has not been possible so far, due to the lack of a suitable depth database. This paper addresses this issue, proposing to opt for synthetically generated images rather than collecting by hand a 2.5D large scale database. While being clearly a proxy for real data, synthetic images allow to trade quality for quantity, making it possible to generate a virtually infinite amount of data. We show that the filters learned from such data collection, using the very same architecture typically used on visual data, learns very different filters, resulting in depth features (a) able to better characterize the different facets of depth images, and (b) complementary with respect to those derived from CNNs pre-trained on 2D datasets. Experiments on two publicly available databases show the power of our approach.

Connected through hyperedge HE-1 (induced by source paper "learning to see across domains and modalities"):
  with members:
    - "boosting domain adaptation by discovering latent domains"
    - "generalizing across domains via cross gradient training"
    - "learning from noisy web data with category level supervision"
    - "kitting in the wild through online domain adaptation"
    - "robust place categorization with deep domain generalization"
    - "deep hashing network for unsupervised domain adaptation"
    - "cycada cycle consistent adversarial domain adaptation"
    - "deep layer aggregation"

Connected through hyperedge HE-2 (induced by source paper "are we done with object recognition the icub robot s perspective"):
  with members:
    - "robotic tactile perception of object properties a review"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "deep image retrieval learning global representations for image search"
    - "improved deep learning of object category using pose information"
    - "the curious robot learning visual representations via physical interactions"
    - "physically based rendering for indoor scene understanding using convolutional neural networks"
    - "learning to recognize objects by retaining other factors of variation"
    - "scene recognition with cnns objects scales and dataset bias"

Connected through hyperedge HE-3 (induced by source paper "human action recognition in drone videos using a few aerial training examples"):
  with members:
    - "uav gesture a dataset for uav control and gesture recognition"
    - "real world anomaly detection in surveillance videos"
    - "scaling egocentric vision the epic kitchens dataset"
    - "multi fiber networks for video recognition"
    - "disjoint multi task learning between heterogeneous human centric tasks"
    - "human motion capture using a drone"
    - "learning object localization and 6d pose estimation from simulation and weakly labeled real images"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-4 (induced by source paper "semi automatic labeling for deep learning in robotics"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "fully convolutional adaptation networks for semantic segmentation"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "a self supervised learning system for object detection using physics simulation and multi view pose estimation"
    - "focal loss for dense object detection"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "adversarial discriminative domain adaptation"
    - "bb8 a scalable accurate robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth"

Connected through hyperedge HE-5 (induced by source paper "keep it unreal bridging the realism gap for 2 5d recognition with geometry priors only"):
  with members:
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "domain adaptation for visual applications a comprehensive survey"
    - "3d object instance recognition and pose estimation using triplet loss with dynamic margin"
    - "t less an rgb d dataset for 6d pose estimation of texture less objects"
    - "triple generative adversarial nets"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "adversarial discriminative domain adaptation"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "recurrent convolutional fusion for rgb d object recognition"):
  with members:
    - "easylabel a semi automatic pixel wise object annotation tool for creating robotic rgb d datasets"
    - "mask r cnn"
    - "correlated and individual multi modal deep learning for rgb d object recognition"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "benchmarking in manipulation research using the yale cmu berkeley object and model set"
    - "inside outside net detecting objects in context with skip pooling and recurrent neural networks"
    - "deep residual learning for image recognition"
    - "multimodal deep learning for robust rgb d object recognition"

Connected through hyperedge HE-7 (induced by source paper "a recurrent multi scale approach to rbg d object recognition"):
  with members:
    - "recurrent convolutional fusion for rgb d object recognition"
    - "mask r cnn"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "correlated and individual multi modal deep learning for rgb d object recognition"
    - "an overview of gradient descent optimization algorithms"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "multimodal deep learning for robust rgb d object recognition"

Connected through hyperedge HE-8 (induced by source paper "kitting in the wild through online domain adaptation"):
  with members:
    - "best sources forward domain generalization through source specific nets"
    - "boosting domain adaptation by discovering latent domains"
    - "adaptive deep learning through visual domain localization"
    - "robust place categorization with deep domain generalization"
    - "deeper broader and artier domain generalization"
    - "just dial domain alignment layers for unsupervised domain adaptation"
    - "icarl incremental classifier and representation learning"
    - "revisiting batch normalization for practical domain adaptation"

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)