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: "cross stitch networks for multi task learning"
  Abstract: Multi-task learning in Convolutional Networks has displayed remarkable success in the field of recognition. This success can be largely attributed to learning shared representations from multiple supervisory tasks. However, existing multi-task approaches rely on enumerating multiple network architectures specific to the tasks at hand, that do not generalize. In this paper, we propose a principled approach to learn shared representations in ConvNets using multi-task learning. Specifically, we propose a new sharing unit: "cross-stitch" unit. These units combine the activations from multiple networks and can be trained end-to-end. A network with cross-stitch units can learn an optimal combination of shared and task-specific representations. Our proposed method generalizes across multiple tasks and shows dramatically improved performance over baseline methods for categories with few training examples.

Connected through hyperedge HE-1 (induced by source paper "mti net multi scale task interaction networks for multi task learning"):
  with members:
    - "which tasks should be learned together in multi task learning"
    - "branched multi task networks deciding what layers to share"
    - "deep high resolution representation learning for visual recognition"
    - "pattern affinitive propagation across depth surface normal and semantic segmentation"
    - "deep high resolution representation learning for human pose estimation"
    - "panoptic feature pyramid networks"
    - "attentive single tasking of multiple tasks"
    - "a modulation module for multi task learning with applications in image retrieval"

Connected through hyperedge HE-2 (induced by source paper "deep multicameral decoding for localizing unoccluded object instances from a single rgb image"):
  with members:
    - "fully dense unet for 2 d sparse photoacoustic tomography artifact removal"
    - "dual graph convolutional network for semantic segmentation"
    - "pointrend image segmentation as rendering"
    - "quantized densely connected u nets for efficient landmark localization"
    - "object segmentation in depth maps with one user click and a synthetically trained fully convolutional network"
    - "mvtec d2s densely segmented supermarket dataset"
    - "deep ordinal regression network for monocular depth estimation"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"

Connected through hyperedge HE-3 (induced by source paper "dynamic task weighting methods for multi task networks in autonomous driving systems"):
  with members:
    - "multinet multi stream feature aggregation and geometric loss strategy for multi task learning"
    - "real time joint object detection and semantic segmentation network for automated driving"
    - "which tasks should be learned together in multi task learning"
    - "auto deeplab hierarchical neural architecture search for semantic image segmentation"
    - "soilingnet soiling detection on automotive surround view cameras"
    - "taskonomy disentangling task transfer learning"
    - "autoaugment learning augmentation policies from data"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"

Connected through hyperedge HE-4 (induced by source paper "multi task learning from videos via efficient inter frame attention"):
  with members:
    - "multinet multi stream feature aggregation and geometric loss strategy for multi task learning"
    - "attentive single tasking of multiple tasks"
    - "attention guided unified network for panoptic segmentation"
    - "ccnet criss cross attention for semantic segmentation"
    - "low latency video semantic segmentation"
    - "video action transformer network"
    - "multi task learning as multi objective optimization"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"

Connected through hyperedge HE-5 (induced by source paper "rgb based semantic segmentation using self supervised depth pre training"):
  with members:
    - "colorization as a proxy task for visual understanding"
    - "unsupervised learning of depth and ego motion from video"
    - "sfm net learning of structure and motion from video"
    - "rethinking atrous convolution for semantic image segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "identity mappings in deep residual networks"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-6 (induced by source paper "learning preference based similarities from face images using siamese multi task cnns"):
  with members:
    - "scut fbp5500 a diverse benchmark dataset for multi paradigm facial beauty prediction"
    - "smoothgrad removing noise by adding noise"
    - "an overview of multi task learning in deep neural networks"
    - "afif4 deep gender classification based on adaboost based fusion of isolated facial features and foggy faces"
    - "faceboxes a cpu real time face detector with high accuracy"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "densely connected convolutional networks"
    - "some like it hot visual guidance for preference prediction"

Connected through hyperedge HE-7 (induced by source paper "beyond without forgetting multi task learning for classification with disjoint datasets"):
  with members:
    - "mixmatch a holistic approach to semi supervised learning"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "multi task multi label and multi domain learning with residual convolutional networks for emotion recognition"
    - "multi task learning of pairwise sequence classification tasks over disparate label spaces"
    - "universal representations the missing link between faces text planktons and cat breeds"
    - "semisupervised feature analysis by mining correlations among multiple tasks"
    - "learning from noisy labels with distillation"
    - "training deep networks for facial expression recognition with crowd sourced label distribution"

Connected through hyperedge HE-8 (induced by source paper "leveraging code generation to improve code retrieval and summarization via dual learning"):
  with members:
    - "coacor code annotation for code retrieval with reinforcement learning"
    - "dual supervised learning for natural language understanding and generation"
    - "jointly learning semantic parser and natural language generator via dual information maximization"
    - "same representation different attentions shareable sentence representation learning from multiple tasks"
    - "staqc a systematically mined question code dataset from stack overflow"
    - "improving automatic source code summarization via deep reinforcement learning"
    - "code2seq generating sequences from structured representations of code"
    - "dual supervised learning"

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)