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: "end to end multi task learning with attention"
  Abstract: In this paper, we propose a novel multi-task learning architecture, which incorporates recent advances in attention mechanisms. Our approach, the Multi-Task Attention Network (MTAN), consists of a single shared network containing a global feature pool, together with task-specific soft-attention modules, which are trainable in an end-to-end manner. These attention modules allow for learning of task-specific features from the global pool, whilst simultaneously allowing for features to be shared across different tasks. The architecture can be built upon any feed-forward neural network, is simple to implement, and is parameter efficient. Experiments on the CityScapes dataset show that our method outperforms several baselines in both single-task and multi-task learning, and is also more robust to the various weighting schemes in the multi-task loss function. We further explore the effectiveness of our method through experiments over a range of task complexities, and show how our method scales well with task complexity compared to baselines.

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 "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-3 (induced by source paper "gradient surgery for multi task learning"):
  with members:
    - "distilling policy distillation"
    - "regularized hierarchical policies for compositional transfer in robotics"
    - "branched multi task networks deciding what layers to share"
    - "meta world a benchmark and evaluation for multi task and meta reinforcement learning"
    - "routing networks and the challenges of modular and compositional computation"
    - "ray interference a source of plateaus in deep reinforcement learning"
    - "attentive single tasking of multiple tasks"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"

Connected through hyperedge HE-4 (induced by source paper "deep multi task multi label cnn for effective facial attribute classification"):
  with members:
    - "multi task learning of cascaded cnn for facial attribute classification"
    - "segment based methods for facial attribute detection from partial faces"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"
    - "heterogeneous face attribute estimation a deep multi task learning approach"
    - "attributes for improved attributes a multi task network for attribute classification"
    - "leveraging mid level deep representations for predicting face attributes in the wild"
    - "moon a mixed objective optimization network for the recognition of facial attributes"
    - "attribute2image conditional image generation from visual attributes"

Connected through hyperedge HE-5 (induced by source paper "um adapt unsupervised multi task adaptation using adversarial cross task distillation"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "adadepth unsupervised content congruent adaptation for depth estimation"
    - "image to image translation for domain adaptation"
    - "analyzing modular cnn architectures for joint depth prediction and semantic segmentation"
    - "domain adaptation for visual applications a comprehensive survey"
    - "no more discrimination cross city adaptation of road scene segmenters"
    - "cycada cycle consistent adversarial domain adaptation"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-6 (induced by source paper "d textbf s 3 l deep self semi supervised learning for image recognition"):
  with members:
    - "small data challenges in big data era a survey of recent progress on unsupervised and semi supervised methods"
    - "s4l self supervised semi supervised learning"
    - "a meta approach to defend noisy labels by the manifold regularizer psdr"
    - "unsupervised label noise modeling and loss correction"
    - "pushing the right boundaries matters wasserstein adversarial training for label noise"
    - "weight standardization"
    - "understanding and utilizing deep neural networks trained with noisy labels"
    - "revisiting self supervised visual representation learning"

Connected through hyperedge HE-7 (induced by source paper "branched multi task networks deciding what layers to share"):
  with members:
    - "stochastic filter groups for multi task cnns learning specialist and generalist convolution kernels"
    - "representation similarity analysis for efficient task taxonomy transfer learning"
    - "feature partitioning for efficient multi task architectures"
    - "attentive single tasking of multiple tasks"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "evolutionary architecture search for deep multitask networks"
    - "regularized evolution for image classifier architecture search"
    - "taskonomy disentangling task transfer learning"

Connected through hyperedge HE-8 (induced by source paper "paying more attention to motion attention distillation for learning video representations"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deeply supervised salient object detection with short connections"
    - "end to end learning of motion representation for video understanding"
    - "what have we learned from deep representations for action recognition"
    - "on the effectiveness of task granularity for transfer learning"
    - "modality distillation with multiple stream networks for action recognition"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "quo vadis action recognition a new model and the kinetics dataset"

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)