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: "pseudo task augmentation from deep multitask learning to intratask sharing and back"
  Abstract: Deep multitask learning boosts performance by sharing learned structure across related tasks. This paper adapts ideas from deep multitask learning to the setting where only a single task is available. The method is formalized as pseudo-task augmentation, in which models are trained with multiple decoders for each task. Pseudo-tasks simulate the effect of training towards closely-related tasks drawn from the same universe. In a suite of experiments, pseudo-task augmentation is shown to improve performance on single-task learning problems. When combined with multitask learning, further improvements are achieved, including state-of-the-art performance on the CelebA dataset, showing that pseudo-task augmentation and multitask learning have complementary value. All in all, pseudo-task augmentation is a broadly applicable and efficient way to boost performance in deep learning systems.

Connected through hyperedge HE-1 (induced by source paper "frosting weights for better continual training"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "meta learning representations for continual learning"
    - "which tasks should be learned together in multi task learning"
    - "online continual learning with maximally interfered retrieval"
    - "continual learning with tiny episodic memories"
    - "supportnet solving catastrophic forgetting in class incremental learning with support data"
    - "model distillation with knowledge transfer from face classification to alignment and verification"
    - "tree cnn a hierarchical deep convolutional neural network for incremental learning"

Connected through hyperedge HE-2 (induced by source paper "modular universal reparameterization deep multi task learning across diverse domains"):
  with members:
    - "evolutionary architecture search for deep multitask networks"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "routing networks adaptive selection of non linear functions for multi task learning"
    - "learning multiple visual domains with residual adapters"
    - "regularizing and optimizing lstm language models"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"
    - "distral robust multitask reinforcement learning"
    - "one model to learn them all"

Connected through hyperedge HE-3 (induced by source paper "constructing multiple tasks for augmentation improving neural image classification with k means features"):
  with members:
    - "understanding and improving interpolation in autoencoders via an adversarial regularizer"
    - "deep clustering for unsupervised learning of visual features"
    - "unsupervised learning via meta learning"
    - "learning to generalize meta learning for domain generalization"
    - "prototypical networks for few shot learning"
    - "a survey on multi task learning"
    - "an overview of multi task learning in deep neural networks"
    - "learning to learn by gradient descent by gradient descent"

Connected through hyperedge HE-4 (induced by source paper "exploring correlations in multiple facial attributes through graph attention network"):
  with members:
    - "augmentor an image augmentation library for machine learning"
    - "an overview of multi task learning in deep neural networks"
    - "fully adaptive feature sharing in multi task networks with applications in person attribute classification"
    - "cross stitch networks for multi task learning"
    - "imagenet pre trained models with batch normalization"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "semi supervised classification with graph convolutional networks"
    - "trace norm regularised deep multi task learning"

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.LG(Machine Learning)