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: "beyond shared hierarchies deep multitask learning through soft layer ordering"
  Abstract: Existing deep multitask learning (MTL) approaches align layers shared between tasks in a parallel ordering. Such an organization significantly constricts the types of shared structure that can be learned. The necessity of parallel ordering for deep MTL is first tested by comparing it with permuted ordering of shared layers. The results indicate that a flexible ordering can enable more effective sharing, thus motivating the development of a soft ordering approach, which learns how shared layers are applied in different ways for different tasks. Deep MTL with soft ordering outperforms parallel ordering methods across a series of domains. These results suggest that the power of deep MTL comes from learning highly general building blocks that can be assembled to meet the demands of each task.

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 "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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "feature partitioning for efficient multi task architectures"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "multi task zipping via layer wise neuron sharing"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "combating catastrophic forgetting with developmental compression"
    - "unifying and merging well trained deep neural networks for inference stage"
    - "towards automated deep learning efficient joint neural architecture and hyperparameter search"
    - "evolutionary architecture search for deep multitask networks"

Connected through hyperedge HE-4 (induced by source paper "deep elastic networks with model selection for multi task learning"):
  with members:
    - "deep virtual networks for memory efficient inference of multiple tasks"
    - "multi task zipping via layer wise neuron sharing"
    - "maskconnect connectivity learning by gradient descent"
    - "a novel channel pruning method for deep neural network compression"
    - "packnet adding multiple tasks to a single network by iterative pruning"
    - "routing networks adaptive selection of non linear functions for multi task learning"
    - "focal loss for dense object detection"
    - "blockdrop dynamic inference paths in residual networks"

Connected through hyperedge HE-5 (induced by source paper "modular universal reparameterization deep multi task learning across diverse domains"):
  with members:
    - "evolutionary architecture search for deep multitask networks"
    - "pseudo task augmentation from deep multitask learning to intratask sharing and back"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "evolutionary neural automl for deep learning"):
  with members:
    - "evolutionary architecture search for deep multitask networks"
    - "regularized evolution for image classifier architecture search"
    - "nsga net neural architecture search using multi objective genetic algorithm"
    - "nsga net a multi objective genetic algorithm for neural architecture search"
    - "evolving deep neural networks"
    - "large scale evolution of image classifiers"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"

Connected through hyperedge HE-7 (induced by source paper "context aware multipath networks"):
  with members:
    - "deep learning for classical japanese literature"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "pad net multi tasks guided prediction and distillation network for simultaneous depth estimation and scene parsing"
    - "packnet adding multiple tasks to a single network by iterative pruning"
    - "blockdrop dynamic inference paths in residual networks"
    - "dynamic routing between capsules"
    - "convolutional networks with adaptive inference graphs"
    - "learning multiple visual domains with residual adapters"

Connected through hyperedge HE-8 (induced by source paper "task driven modular networks for zero shot compositional learning"):
  with members:
    - "from classical to generalized zero shot learning a simple adaptation process"
    - "houdini lifelong learning as program synthesis"
    - "attributes as operators factorizing unseen attribute object compositions"
    - "tafe net task aware feature embeddings for efficient learning and inference"
    - "maskconnect connectivity learning by gradient descent"
    - "the devil is in the tails fine grained classification in the wild"
    - "routing networks adaptive selection of non linear functions for multi task learning"
    - "film visual reasoning with a general conditioning layer"

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)