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 domain self supervised multi task feature learning using synthetic imagery"
  Abstract: In human learning, it is common to use multiple sources of information jointly. However, most existing feature learning approaches learn from only a single task. In this paper, we propose a novel multi-task deep network to learn generalizable high-level visual representations. Since multi-task learning requires annotations for multiple properties of the same training instance, we look to synthetic images to train our network. To overcome the domain difference between real and synthetic data, we employ an unsupervised feature space domain adaptation method based on adversarial learning. Given an input synthetic RGB image, our network simultaneously predicts its surface normal, depth, and instance contour, while also minimizing the feature space domain differences between real and synthetic data. Through extensive experiments, we demonstrate that our network learns more transferable representations compared to single-task baselines. Our learned representation produces state-of-the-art transfer learning results on PASCAL VOC 2007 classification and 2012 detection.

Connected through hyperedge HE-1 (induced by source paper "unsupervised domain adaptation for mobile semantic segmentation based on cycle consistency and feature alignment"):
  with members:
    - "crdoco pixel level domain transfer with cross domain consistency"
    - "domain bridge for unpaired image to image translation and unsupervised domain adaptation"
    - "constructing self motivated pyramid curriculums for cross domain semantic segmentation a non adversarial approach"
    - "adversarial learning and self teaching techniques for domain adaptation in semantic segmentation"
    - "significance aware information bottleneck for domain adaptive semantic segmentation"
    - "dada depth aware domain adaptation in semantic segmentation"
    - "ace adapting to changing environments for semantic segmentation"
    - "learning to adapt structured output space for semantic segmentation"

Connected through hyperedge HE-2 (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-3 (induced by source paper "learning to generalize one sample at a time with self supervision"):
  with members:
    - "episodic training for domain generalization"
    - "adagraph unifying predictive and continuous domain adaptation through graphs"
    - "self supervised generalisation with meta auxiliary learning"
    - "domain generalization by solving jigsaw puzzles"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "grasp2vec learning object representations from self supervised grasping"
    - "boosting self supervised learning via knowledge transfer"
    - "open set domain adaptation by backpropagation"

Connected through hyperedge HE-4 (induced by source paper "attentive single tasking of multiple tasks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "adversarial collaboration joint unsupervised learning of depth camera motion optical flow and motion segmentation"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "taskonomy disentangling task transfer learning"
    - "unified perceptual parsing for scene understanding"
    - "efficient video object segmentation via network modulation"

Connected through hyperedge HE-5 (induced by source paper "when does self supervision improve few shot learning"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "surprising effectiveness of few image unsupervised feature learning"
    - "meta learning with differentiable convex optimization"
    - "few shot learning with localization in realistic settings"
    - "boosting few shot visual learning with self supervision"
    - "large scale adversarial representation learning"
    - "unsupervised pre training of image features on non curated data"
    - "domain generalization by solving jigsaw puzzles"

Connected through hyperedge HE-6 (induced by source paper "a critical analysis of self supervision or what we can learn from a single image"):
  with members:
    - "scaling and benchmarking self supervised visual representation learning"
    - "singan learning a generative model from a single natural image"
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "cross pixel optical flow similarity for self supervised learning"
    - "unsupervised representation learning by predicting image rotations"
    - "exploring the limits of weakly supervised pretraining"

Connected through hyperedge HE-7 (induced by source paper "self labelling via simultaneous clustering and representation learning"):
  with members:
    - "aet vs aed unsupervised representation learning by auto encoding transformations rather than data"
    - "unsupervised deep learning by neighbourhood discovery"
    - "surprising effectiveness of few image unsupervised feature learning"
    - "data efficient image recognition with contrastive predictive coding"
    - "a critical analysis of self supervision or what we can learn from a single image"
    - "local aggregation for unsupervised learning of visual embeddings"
    - "learning representations by maximizing mutual information across views"
    - "revisiting self supervised visual representation learning"

Connected through hyperedge HE-8 (induced by source paper "surprising effectiveness of few image unsupervised feature learning"):
  with members:
    - "boosting self supervised learning via knowledge transfer"
    - "deep clustering for unsupervised learning of visual features"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "unsupervised representation learning by predicting image rotations"
    - "self supervised feature learning by learning to spot artifacts"
    - "transitive invariance for self supervised visual representation learning"
    - "improvements to context based self supervised learning"
    - "deep image prior"

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)