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: "multinet real time joint semantic reasoning for autonomous driving"
  Abstract: While most approaches to semantic reasoning have focused on improving performance, in this paper we argue that computational times are very important in order to enable real time applications such as autonomous driving. Towards this goal, we present an approach to joint classification, detection and semantic segmentation via a unified architecture where the encoder is shared amongst the three tasks. Our approach is very simple, can be trained end-to-end and performs extremely well in the challenging KITTI dataset, outperforming the state-of-the-art in the road segmentation task. Our approach is also very efficient, taking less than 100 ms to perform all tasks.

Connected through hyperedge HE-1 (induced by source paper "self supervised learning for autonomous vehicles perception a conciliation between analytical and learning methods"):
  with members:
    - "panoptic segmentation"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "deep clustering for unsupervised learning of visual features"
    - "dimensionality driven learning with noisy labels"
    - "digging into self supervised monocular depth estimation"
    - "self supervised learning for stereo matching with self improving ability"
    - "unsupervised learning of depth and ego motion from video"
    - "deep manta a coarse to fine many task network for joint 2d and 3d vehicle analysis from monocular image"

Connected through hyperedge HE-2 (induced by source paper "a large rgb d dataset for semi supervised monocular depth estimation"):
  with members:
    - "dense 3d object reconstruction from a single depth view"
    - "colorization as a proxy task for visual understanding"
    - "data distillation towards omni supervised learning"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "semi supervised deep learning for monocular depth map prediction"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "unsupervised monocular depth estimation with left right consistency"
    - "context encoders feature learning by inpainting"

Connected through hyperedge HE-3 (induced by source paper "a methodological review of visual road recognition procedures for autonomous driving applications"):
  with members:
    - "fast lidar based road detection using fully convolutional neural networks"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "learning a driving simulator"
    - "end to end learning for self driving cars"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-4 (induced by source paper "progressive lidar adaptation for road detection"):
  with members:
    - "lidar camera fusion for road detection using fully convolutional neural networks"
    - "fast lidar based road detection using fully convolutional neural networks"
    - "dilated residual networks"
    - "understanding convolution for semantic segmentation"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "pyramid scene parsing network"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "unsupervised synthesis of anomalies in videos transforming the normal"):
  with members:
    - "real world anomaly detection in surveillance videos"
    - "dropout inference in bayesian neural networks with alpha divergences"
    - "abnormal event detection in videos using spatiotemporal autoencoder"
    - "learning from simulated and unsupervised images through adversarial training"
    - "learning features by watching objects move"
    - "learning temporal regularity in video sequences"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "deep representation learning for road detection through siamese network"):
  with members:
    - "dynamically visual disambiguation of keyword based image search"
    - "extracting visual knowledge from the internet making sense of image data"
    - "embedding structured contour and location prior in siamesed fully convolutional networks for road detection"
    - "aspect aware latent factor model rating prediction with ratings and reviews"
    - "neural compatibility modeling with attentive knowledge distillation"
    - "fast lidar based road detection using fully convolutional neural networks"
    - "exploiting web images for dataset construction a domain robust approach"
    - "mask r cnn"

Connected through hyperedge HE-7 (induced by source paper "triply supervised decoder networks for joint detection and segmentation"):
  with members:
    - "panoptic segmentation"
    - "panoptic feature pyramid networks"
    - "learning a discriminative feature network for semantic segmentation"
    - "context encoding for semantic segmentation"
    - "biseg simultaneous instance segmentation and semantic segmentation with fully convolutional networks"
    - "focal loss for dense object detection"
    - "blitznet a real time deep network for scene understanding"
    - "large kernel matters improve semantic segmentation by global convolutional network"

Connected through hyperedge HE-8 (induced by source paper "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"):
  with members:
    - "structured attention guided convolutional neural fields for monocular depth estimation"
    - "learning deep structured multi scale features using attention gated crfs for contour prediction"
    - "analyzing modular cnn architectures for joint depth prediction and semantic segmentation"
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "recurrent scene parsing with perspective understanding in the loop"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "monocular depth estimation with hierarchical fusion of dilated cnns and soft weighted sum inference"
    - "heterogeneous face attribute estimation a deep multi task learning approach"

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)