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: "video scene parsing with predictive feature learning"
  Abstract: In this work, we address the challenging video scene parsing problem by developing effective representation learning methods given limited parsing annotations. In particular, we contribute two novel methods that constitute a unified parsing framework. (1) \textbf{Predictive feature learning}} from nearly unlimited unlabeled video data. Different from existing methods learning features from single frame parsing, we learn spatiotemporal discriminative features by enforcing a parsing network to predict future frames and their parsing maps (if available) given only historical frames. In this way, the network can effectively learn to capture video dynamics and temporal context, which are critical clues for video scene parsing, without requiring extra manual annotations. (2) \textbf{Prediction steering parsing}} architecture that effectively adapts the learned spatiotemporal features to scene parsing tasks and provides strong guidance for any off-the-shelf parsing model to achieve better video scene parsing performance. Extensive experiments over two challenging datasets, Cityscapes and Camvid, have demonstrated the effectiveness of our methods by showing significant improvement over well-established baselines.

Connected through hyperedge HE-1 (induced by source paper "implicit label augmentation on partially annotated clips via temporally adaptive features learning"):
  with members:
    - "panoptic segmentation"
    - "data augmentation using learned transformations for one shot medical image segmentation"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "cornernet detecting objects as paired keypoints"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"

Connected through hyperedge HE-2 (induced by source paper "prediction tracking segmentation"):
  with members:
    - "feature selective anchor free module for single shot object detection"
    - "amc automl for model compression and acceleration on mobile devices"
    - "bounding box regression with uncertainty for accurate object detection"
    - "distractor aware siamese networks for visual object tracking"
    - "a twofold siamese network for real time object tracking"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "fast online object tracking and segmentation a unifying approach"
    - "towards a better match in siamese network based visual object tracker"

Connected through hyperedge HE-3 (induced by source paper "deep multiphase level set for scene parsing"):
  with members:
    - "fully convolutional adaptation networks for semantic segmentation"
    - "learning deep representations for semantic image parsing a comprehensive overview"
    - "ocnet object context network for scene parsing"
    - "unified perceptual parsing for scene understanding"
    - "moe spnet a mixture of experts scene parsing network"
    - "searching for efficient multi scale architectures for dense image prediction"
    - "context encoding for semantic segmentation"
    - "loss max pooling for semantic image segmentation"

Connected through hyperedge HE-4 (induced by source paper "segmenting the future"):
  with members:
    - "predicting future instance segmentations by forecasting convolutional features"
    - "action agnostic human pose forecasting"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "bayesian prediction of future street scenes using synthetic likelihoods"
    - "context encoding for semantic segmentation"
    - "d3d distilled 3d networks for video action recognition"
    - "future semantic segmentation with convolutional lstm"
    - "learning spatio temporal representation with pseudo 3d residual networks"

Connected through hyperedge HE-5 (induced by source paper "every frame counts joint learning of video segmentation and optical flow"):
  with members:
    - "bridging stereo matching and optical flow via spatiotemporal correspondence"
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "selflow self supervised learning of optical flow"
    - "deep spatio temporal random fields for efficient video segmentation"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "low latency video semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "improving semantic segmentation via video propagation and label relaxation"

Connected through hyperedge HE-6 (induced by source paper "enhancing traffic scene predictions with generative adversarial networks"):
  with members:
    - "sequential attend infer repeat generative modelling of moving objects"
    - "learning to forecast and refine residual motion for image to video generation"
    - "reduced gate convolutional lstm using predictive coding for spatiotemporal prediction"
    - "yolov3 an incremental improvement"
    - "futuregan anticipating the future frames of video sequences using spatio temporal 3d convolutions in progressively growing gans"
    - "disentangling propagation and generation for video prediction"
    - "novel video prediction for large scale scene using optical flow"
    - "bayesian prediction of future street scenes using synthetic likelihoods"

Connected through hyperedge HE-7 (induced by source paper "cross attention network for semantic segmentation"):
  with members:
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "concurrent spatial and channel squeeze excitation in fully convolutional networks"
    - "dual attention network for scene segmentation"
    - "pyramid attention network for semantic segmentation"
    - "context encoding for semantic segmentation"
    - "recurrent scene parsing with perspective understanding in the loop"
    - "icnet for real time semantic segmentation on high resolution images"

Connected through hyperedge HE-8 (induced by source paper "boosting real time driving scene parsing with shared semantics"):
  with members:
    - "short term prediction and multi camera fusion on semantic grids"
    - "sfnet learning object aware semantic correspondence"
    - "a lightweight optical flow cnn revisiting data fidelity and regularization"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "learning correspondence from the cycle consistency of time"
    - "searching for mobilenetv3"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "dynamic video segmentation network"

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)