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: "branched multi task networks deciding what layers to share"
  Abstract: In the context of deep learning, neural networks with multiple branches have been used that each solve different tasks. Such ramified networks typically start with a number of shared layers, after which different tasks branch out into their own sequence of layers. As the number of possible network configurations is combinatorially large, prior work has often relied on ad hoc methods to determine the level of layer sharing. #R##N#This work proposes a novel method to assess the relatedness of tasks in a principled way. We base the relatedness of a task pair on the usefulness of a set of features of one task for the other, and vice versa. The resulting task affinities are used for the automated construction of a branched multi-task network in which deeper layers gradually grow more task-specific. Our multi-task network outperforms the state-of-the-art on CelebA. Additionally, the layer sharing schemes devised by our method outperform common multi-task learning models which were constructed ad hoc. We include additional experiments on Cityscapes and SUN RGB-D to illustrate the wide applicability of our approach. Code and trained models for this paper are made available this https URL

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"
    - "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"
    - "taskonomy disentangling task transfer learning"

Connected through hyperedge HE-2 (induced by source paper "gradient surgery for multi task learning"):
  with members:
    - "distilling policy distillation"
    - "regularized hierarchical policies for compositional transfer in robotics"
    - "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"
    - "the natural language decathlon multitask learning as question answering"

Connected through hyperedge HE-3 (induced by source paper "deeper task specificity improves joint entity and relation extraction"):
  with members:
    - "span based joint entity and relation extraction with transformer pre training"
    - "an empirical evaluation of multi task learning in deep neural networks for natural language processing"
    - "entity relation extraction as multi turn question answering"
    - "a general framework for information extraction using dynamic span graphs"
    - "multi task identification of entities relations and coreference for scientific knowledge graph construction"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "deep contextualized word representations"
    - "joint entity recognition and relation extraction as a multi head selection problem"

Connected through hyperedge HE-4 (induced by source paper "adashare learning what to share for efficient deep multi task learning"):
  with members:
    - "stochastic filter groups for multi task cnns learning specialist and generalist convolution kernels"
    - "deep elastic networks with model selection for multi task learning"
    - "which tasks should be learned together in multi task learning"
    - "many task learning with task routing"
    - "evolutionary architecture search for deep multitask networks"
    - "taskonomy disentangling task transfer learning"
    - "exploring shared structures and hierarchies for multiple nlp tasks"
    - "spottune transfer learning through adaptive fine tuning"

Connected through hyperedge HE-5 (induced by source paper "regularizing deep multi task networks using orthogonal gradients"):
  with members:
    - "meta learning representations for continual learning"
    - "which tasks should be learned together in multi task learning"
    - "on the variance of the adaptive learning rate and beyond"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "taskonomy disentangling task transfer learning"
    - "end to end multi task learning with attention"
    - "multi task learning as multi objective optimization"
    - "learning to learn without forgetting by maximizing transfer and minimizing interference"

Connected through hyperedge HE-6 (induced by source paper "disentangling redundancy for multi task pruning"):
  with members:
    - "multi task zipping via layer wise neuron sharing"
    - "unifying and merging well trained deep neural networks for inference stage"
    - "nestdnn resource aware multi tenant on device deep learning for continuous mobile vision"
    - "compressing neural networks using the variational information bottleneck"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "estimating mixture entropy with pairwise distances"
    - "learning structured sparsity in deep neural networks"
    - "eie efficient inference engine on compressed deep neural network"

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)