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: "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
  Abstract: Numerous deep learning applications benefit from multi-task learning with multiple regression and classification objectives. In this paper we make the observation that the performance of such systems is strongly dependent on the relative weighting between each task's loss. Tuning these weights by hand is a difficult and expensive process, making multi-task learning prohibitive in practice. We propose a principled approach to multi-task deep learning which weighs multiple loss functions by considering the homoscedastic uncertainty of each task. This allows us to simultaneously learn various quantities with different units or scales in both classification and regression settings. We demonstrate our model learning per-pixel depth regression, semantic and instance segmentation from a monocular input image. Perhaps surprisingly, we show our model can learn multi-task weightings and outperform separate models trained individually on each task.

Connected through hyperedge HE-1 (induced by source paper "spatial sampling network for fast scene understanding"):
  with members:
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "benchmark analysis of representative deep neural network architectures"
    - "guided upsampling network for real time semantic segmentation"
    - "contextnet exploring context and detail for semantic segmentation in real time"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "path aggregation network for instance segmentation"
    - "efficient dense modules of asymmetric convolution for real time semantic segmentation"
    - "fast scene understanding for autonomous driving"

Connected through hyperedge HE-2 (induced by source paper "deep neural networks for marine debris detection in sonar images"):
  with members:
    - "on the convergence of adam and beyond"
    - "taking human out of learning applications a survey on automated machine learning"
    - "darts differentiable architecture search"
    - "towards understanding regularization in batch normalization"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "learning transferable architectures for scalable image recognition"
    - "dsod learning deeply supervised object detectors from scratch"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-3 (induced by source paper "efficient multi domain network learning by covariance normalization"):
  with members:
    - "taskonomy disentangling task transfer learning"
    - "boosting domain adaptation by discovering latent domains"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "efficient parametrization of multi domain deep neural networks"
    - "universal representations the missing link between faces text planktons and cat breeds"
    - "progressive neural networks"
    - "cycada cycle consistent adversarial domain adaptation"
    - "incremental learning through deep adaptation"

Connected through hyperedge HE-4 (induced by source paper "exploring uncertainty in conditional multi modal retrieval systems"):
  with members:
    - "leveraging heteroscedastic aleatoric uncertainties for robust real time lidar 3d object detection"
    - "features for multi target multi camera tracking and re identification"
    - "two stream self supervised learning for action recognition"
    - "exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation"
    - "uncertainty estimates and multi hypotheses networks for optical flow"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "efficient uncertainty estimation for semantic segmentation in videos"
    - "in defense of the triplet loss for person re identification"

Connected through hyperedge HE-5 (induced by source paper "libra r cnn towards balanced learning for object detection"):
  with members:
    - "hybrid task cascade for instance segmentation"
    - "cornernet detecting objects as paired keypoints"
    - "deep feature pyramid reconfiguration for object detection"
    - "acquisition of localization confidence for accurate object detection"
    - "path aggregation network for instance segmentation"
    - "sniper efficient multi scale training"
    - "non local neural networks"
    - "focal loss for dense object detection"

Connected through hyperedge HE-6 (induced by source paper "autosem automatic task selection and mixing in multi task learning"):
  with members:
    - "the natural language decathlon multitask learning as question answering"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "dynamic multi level multi task learning for sentence simplification"
    - "automated curriculum learning for neural networks"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "identifying beneficial task relations for multi task learning in deep neural networks"
    - "a tutorial on thompson sampling"

Connected through hyperedge HE-7 (induced by source paper "instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "affinity derivation and graph merge for instance segmentation"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "path aggregation network for instance segmentation"
    - "semi convolutional operators for instance segmentation"
    - "recurrent pixel embedding for instance grouping"
    - "pixelwise instance segmentation with a dynamically instantiated network"
    - "what uncertainties do we need in bayesian deep learning for computer vision"

Connected through hyperedge HE-8 (induced by source paper "graph convolution for multimodal information extraction from visually rich documents"):
  with members:
    - "n ary relation extraction using graph state lstm"
    - "chargrid towards understanding 2d documents"
    - "graph2seq graph to sequence learning with attention based neural networks"
    - "image generation from scene graphs"
    - "cross sentence n ary relation extraction with graph lstms"
    - "attention is all you need"
    - "joint extraction of entities and relations based on a novel tagging scheme"
    - "cloudscan a configuration free invoice analysis system using recurrent 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)