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: "segnet a deep convolutional encoder decoder architecture for robust semantic pixel wise labelling"
  Abstract: We propose a novel deep architecture, SegNet, for semantic pixel wise image labelling. SegNet has several attractive properties; (i) it only requires forward evaluation of a fully learnt function to obtain smooth label predictions, (ii) with increasing depth, a larger context is considered for pixel labelling which improves accuracy, and (iii) it is easy to visualise the effect of feature activation(s) in the pixel label space at any depth. SegNet is composed of a stack of encoders followed by a corresponding decoder stack which feeds into a soft-max classification layer. The decoders help map low resolution feature maps at the output of the encoder stack to full input image size feature maps. This addresses an important drawback of recent deep learning approaches which have adopted networks designed for object categorization for pixel wise labelling. These methods lack a mechanism to map deep layer feature maps to input dimensions. They resort to ad hoc methods to upsample features, e.g. by replication. This results in noisy predictions and also restricts the number of pooling layers in order to avoid too much upsampling and thus reduces spatial context. SegNet overcomes these problems by learning to map encoder outputs to image pixel labels. We test the performance of SegNet on outdoor RGB scenes from CamVid, KITTI and indoor scenes from the NYU dataset. Our results show that SegNet achieves state-of-the-art performance even without use of additional cues such as depth, video frames or post-processing with CRF models.

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"
    - "depth cnns for rgb d scene recognition learning from scratch better than transferring from rgb cnns"

Connected through hyperedge HE-2 (induced by source paper "scene understanding for autonomous manipulation with deep learning"):
  with members:
    - "v2cnet a deep learning framework to translate videos to commands for robotic manipulation"
    - "visual affordance and function understanding a survey"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "semantic decomposition and recognition of long and complex manipulation action sequences"
    - "one shot visual imitation learning via meta learning"
    - "third person imitation learning"
    - "deep imitation learning for complex manipulation tasks from virtual reality teleoperation"
    - "video captioning with transferred semantic attributes"

Connected through hyperedge HE-3 (induced by source paper "context integrated and feature refined network for lightweight object parsing"):
  with members:
    - "connnet a long range relation aware pixel connectivity network for salient segmentation"
    - "vortex pooling improving context representation in semantic segmentation"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "rtseg real time semantic segmentation comparative study"
    - "image super resolution using very deep residual channel attention networks"
    - "guided upsampling network for real time semantic segmentation"
    - "driving scene perception network real time joint detection depth estimation and semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "multi modal attention based fusion model for semantic segmentation of rgb depth images"):
  with members:
    - "depth aware cnn for rgb d segmentation"
    - "depth adaptive deep neural network for semantic segmentation"
    - "pixel wise attentional gating for parsimonious pixel labeling"
    - "cbam convolutional block attention module"
    - "squeeze and excitation networks"
    - "joint 2d 3d semantic data for indoor scene understanding"
    - "lstm cf unifying context modeling and fusion with lstms for rgb d scene labeling"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-5 (induced by source paper "dbsn measuring uncertainty through bayesian learning of deep neural network structures"):
  with members:
    - "functional variational bayesian neural networks"
    - "single path one shot neural architecture search with uniform sampling"
    - "self tuning networks bilevel optimization of hyperparameters using structured best response functions"
    - "regularized evolution for image classifier architecture search"
    - "proxylessnas direct neural architecture search on target task and hardware"
    - "eigenvalue corrected noisy natural gradient"
    - "darts differentiable architecture search"
    - "snas stochastic neural architecture search"

Connected through hyperedge HE-6 (induced by source paper "learning densities in feature space for reliable segmentation of indoor scenes"):
  with members:
    - "the fishyscapes benchmark measuring blind spots in semantic segmentation"
    - "volumetric instance aware semantic mapping and 3d object discovery"
    - "panopticfusion online volumetric semantic mapping at the level of stuff and things"
    - "fusion volumetric object level slam"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "mid fusion octree based object level multi instance dynamic slam"

Connected through hyperedge HE-7 (induced by source paper "privacy protection in street view panoramas using depth and multi view imagery"):
  with members:
    - "lidar assisted large scale privacy protection in street view cycloramas"
    - "generative image inpainting with contextual attention"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "multi view generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-8 (induced by source paper "r 3 net a deep network for multioriented vehicle detection in aerial images and videos"):
  with members:
    - "yolov3 an incremental improvement"
    - "vehicle instance segmentation from aerial image and video using a multitask learning residual fully convolutional network"
    - "automatic ship detection in remote sensing images from google earth of complex scenes based on multiscale rotation dense feature pyramid networks"
    - "rifcn recurrent network in fully convolutional network for semantic segmentation of high resolution remote sensing images"
    - "dota a large scale dataset for object detection in aerial images"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "beyond rgb very high resolution urban remote sensing with multimodal deep networks"
    - "mask r cnn"

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)