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: "semantic understanding of scenes through the ade20k dataset"
  Abstract: Scene parsing, or recognizing and segmenting objects and stuff in an image, is one of the key problems in computer vision. Despite the community's efforts in data collection, there are still few image datasets covering a wide range of scenes and object categories with dense and detailed annotations for scene parsing. In this paper, we introduce and analyze the ADE20K dataset, spanning diverse annotations of scenes, objects, parts of objects, and in some cases even parts of parts. A generic network design called Cascade Segmentation Module is then proposed to enable the segmentation networks to parse a scene into stuff, objects, and object parts in a cascade. We evaluate the proposed module integrated within two existing semantic segmentation networks, yielding significant improvements for scene parsing. We further show that the scene parsing networks trained on ADE20K can be applied to a wide variety of scenes and objects.

Connected through hyperedge HE-1 (induced by source paper "ego topo environment affordances from egocentric video"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "scene memory transformer for embodied agents in long horizon tasks"
    - "videograph recognizing minutes long human activities in videos"
    - "anticipation and next action forecasting in video an end to end model with memory"
    - "generative hybrid representations for activity forecasting with no regret learning"
    - "what would you expect anticipating egocentric actions with rolling unrolling lstms and modality attention"
    - "long term feature banks for detailed video understanding"
    - "binge watching scaling affordance learning from sitcoms"

Connected through hyperedge HE-2 (induced by source paper "semantic flow for fast and accurate scene parsing"):
  with members:
    - "expectation maximization attention networks for semantic segmentation"
    - "customizable architecture search for semantic segmentation"
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "panoptic feature pyramid networks"
    - "ocnet object context network for scene parsing"
    - "guided upsampling network for real time semantic segmentation"
    - "espnetv2 a light weight power efficient and general purpose convolutional neural network"
    - "low latency video semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "benchmarking the robustness of semantic segmentation models"):
  with members:
    - "guided curriculum model adaptation and uncertainty aware evaluation for semantic nighttime image segmentation"
    - "adversarial examples are a natural consequence of test error in noise"
    - "gated scnn gated shape cnns for semantic segmentation"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "benchmarking robustness in object detection autonomous driving when winter is coming"
    - "deepcorrect correcting dnn models against image distortions"
    - "semantic foggy scene understanding with synthetic data"
    - "mobilenetv2 inverted residuals and linear bottlenecks"

Connected through hyperedge HE-4 (induced by source paper "scaling and benchmarking self supervised visual representation learning"):
  with members:
    - "revisiting self supervised visual representation learning"
    - "mid level visual representations improve generalization and sample efficiency for learning active tasks"
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"
    - "gibson env real world perception for embodied agents"
    - "mid level visual representations improve generalization and sample efficiency for learning visuomotor policies"
    - "learning to separate object sounds by watching unlabeled video"
    - "unsupervised representation learning by predicting image rotations"

Connected through hyperedge HE-5 (induced by source paper "dual graph convolutional network for semantic segmentation"):
  with members:
    - "global aggregation then local distribution in fully convolutional networks"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "ocnet object context network for scene parsing"
    - "learning a discriminative feature network for semantic segmentation"
    - "adaptive affinity fields for semantic segmentation"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "graph based global reasoning networks"

Connected through hyperedge HE-6 (induced by source paper "glu net global local universal network for dense flow and correspondences"):
  with members:
    - "geometric image correspondence verification by dense pixel matching"
    - "dynamic context correspondence network for semantic alignment"
    - "an empirical evaluation study on the training of sdc features for dense pixel matching"
    - "a lightweight optical flow cnn revisiting data fidelity and regularization"
    - "glampoints greedily learned accurate match points"
    - "semantic attribute matching networks"
    - "what makes good synthetic training data for learning disparity and optical flow estimation"
    - "lf net learning local features from images"

Connected through hyperedge HE-7 (induced by source paper "gff gated fully fusion for semantic segmentation"):
  with members:
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "ocnet object context network for scene parsing"
    - "learning a discriminative feature network for semantic segmentation"
    - "adaptive affinity fields for semantic segmentation"
    - "unified perceptual parsing for scene understanding"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "dual attention network for scene segmentation"

Connected through hyperedge HE-8 (induced by source paper "switchable whitening for deep representation learning"):
  with members:
    - "mmdetection open mmlab detection toolbox and benchmark"
    - "iterative normalization beyond standardization towards efficient whitening"
    - "learning to adapt structured output space for semantic segmentation"
    - "batch kalman normalization towards training deep neural networks with micro batches"
    - "group normalization"
    - "do normalization layers in a deep convnet really need to be distinct"
    - "differentiable learning to normalize via switchable normalization"
    - "batch instance normalization for adaptively style invariant neural networks"

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)