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: "auxiliary tasks in multi task learning"
  Abstract: Multi-task convolutional neural networks (CNNs) have shown impressive results for certain combinations of tasks, such as single-image depth estimation (SIDE) and semantic segmentation. This is achieved by pushing the network towards learning a robust representation that generalizes well to different atomic tasks. We extend this concept by adding auxiliary tasks, which are of minor relevance for the application, to the set of learned tasks. As a kind of additional regularization, they are expected to boost the performance of the ultimately desired main tasks. To study the proposed approach, we picked vision-based road scene understanding (RSU) as an exemplary application. Since multi-task learning requires specialized datasets, particularly when using extensive sets of tasks, we provide a multi-modal dataset for multi-task RSU, called synMT. More than 2.5 $\cdot$ 10^5 synthetic images, annotated with 21 different labels, were acquired from the video game Grand Theft Auto V (GTA V). Our proposed deep multi-task CNN architecture was trained on various combination of tasks using synMT. The experiments confirmed that auxiliary tasks can indeed boost network performance, both in terms of final results and training time.

Connected through hyperedge HE-1 (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-2 (induced by source paper "multidepth single image depth estimation via multi task regression and classification"):
  with members:
    - "auxnet auxiliary tasks enhanced semantic segmentation for automated driving"
    - "deep attention based classification network for robust depth prediction"
    - "on the importance of stereo for accurate depth estimation an efficient semi supervised deep neural network approach"
    - "planenet piece wise planar reconstruction from a single rgb image"
    - "segstereo exploiting semantic information for disparity estimation"
    - "deep ordinal regression network for monocular depth estimation"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "depth estimation via affinity learned with convolutional spatial propagation network"

Connected through hyperedge HE-3 (induced by source paper "semantic segmentation from remote sensor data and the exploitation of latent learning for classification of auxiliary tasks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "self supervised generalisation with meta auxiliary learning"
    - "temporary removal aerial imagery for roof segmentation a large scale dataset towards automatic mapping of buildings"
    - "fine grained visual categorization using meta learning optimization with sample selection of auxiliary data"
    - "automatic pixelwise object labeling for aerial imagery using stacked u nets"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "concurrent spatial and channel squeeze excitation in fully convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "faster training of mask r cnn by focusing on instance boundaries"):
  with members:
    - "path aggregation network for instance segmentation"
    - "a probabilistic u net for segmentation of ambiguous images"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"
    - "an overview of multi task learning in deep neural networks"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "unsupervised monocular depth estimation with left right consistency"
    - "boundary aware instance segmentation"

Connected through hyperedge HE-5 (induced by source paper "auxnet auxiliary tasks enhanced semantic segmentation for automated driving"):
  with members:
    - "effnet an efficient structure for convolutional neural networks"
    - "monocular fisheye camera depth estimation using sparse lidar supervision"
    - "driving scene perception network real time joint detection depth estimation and semantic segmentation"
    - "end to end multi task learning with attention"
    - "multi task learning as multi objective optimization"
    - "monocular depth estimation by learning from heterogeneous datasets"
    - "synscapes a photorealistic synthetic dataset for street scene parsing"
    - "analyzing modular cnn architectures for joint depth prediction and semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "neurall towards a unified model for visual perception in automated driving"):
  with members:
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "model adaptation with synthetic and real data for semantic dense foggy scene understanding"
    - "yolov3 an incremental improvement"
    - "taskonomy disentangling task transfer learning"
    - "learning efficient convolutional networks through network slimming"
    - "universal representations the missing link between faces text planktons and cat breeds"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"
    - "fast scene understanding for autonomous driving"

Connected through hyperedge HE-7 (induced by source paper "self supervised generalisation with meta auxiliary learning"):
  with members:
    - "learning to generalize from sparse and underspecified rewards"
    - "fine grained visual categorization using meta learning optimization with sample selection of auxiliary data"
    - "cinic 10 is not imagenet or cifar 10"
    - "end to end multi task learning with attention"
    - "focal loss for dense object detection"
    - "prototypical networks for few shot learning"
    - "unsupervised learning of depth and ego motion from video"
    - "multi task self supervised visual learning"

Connected through hyperedge HE-8 (induced by source paper "to balance or not to balance a simple yet effective approach for learning with long tailed distributions"):
  with members:
    - "learning imbalanced datasets with label distribution aware margin loss"
    - "boosting few shot visual learning with self supervision"
    - "dynamic curriculum learning for imbalanced data classification"
    - "self supervised generalisation with meta auxiliary learning"
    - "large scale long tailed recognition in an open world"
    - "class balanced loss based on effective number of samples"
    - "revisiting self supervised visual representation learning"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"

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)