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: "many task learning with task routing"
  Abstract: Typical multi-task learning (MTL) methods rely on architectural adjustments and a large trainable parameter set to jointly optimize over several tasks. However, when the number of tasks increases so do the complexity of the architectural adjustments and resource requirements. In this paper, we introduce a method which applies a conditional feature-wise transformation over the convolutional activations that enables a model to successfully perform a large number of tasks. To distinguish from regular MTL, we introduce Many Task Learning (MaTL) as a special case of MTL where more than 20 tasks are performed by a single model. Our method dubbed Task Routing (TR) is encapsulated in a layer we call the Task Routing Layer (TRL), which applied in an MaTL scenario successfully fits hundreds of classification tasks in one model. We evaluate our method on 5 datasets against strong baselines and state-of-the-art approaches.

Connected through hyperedge HE-1 (induced by source paper "multi task learning by a top down control network"):
  with members:
    - "panoptic segmentation"
    - "clevr ref diagnosing visual reasoning with referring expressions"
    - "superposition of many models into one"
    - "attentive single tasking of multiple tasks"
    - "a modulation module for multi task learning with applications in image retrieval"
    - "top down feedback for crowd counting convolutional neural network"
    - "multi task learning as multi objective optimization"
    - "progressive neural networks"

Connected through hyperedge HE-2 (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"
    - "branched multi task networks deciding what layers to share"
    - "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-3 (induced by source paper "learning generalizable representations via diverse supervision"):
  with members:
    - "stochastic filter groups for multi task cnns learning specialist and generalist convolution kernels"
    - "boosting few shot visual learning with self supervision"
    - "lvis a dataset for large vocabulary instance segmentation"
    - "a closer look at few shot classification"
    - "meta learning for semi supervised few shot classification"
    - "multi task learning as multi objective optimization"
    - "unsupervised representation learning by predicting image rotations"
    - "low shot learning from imaginary data"

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)