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: "learning task grouping and overlap in multi task learning"
  Abstract: In the paradigm of multi-task learning, mul- tiple related prediction tasks are learned jointly, sharing information across the tasks. We propose a framework for multi-task learn- ing that enables one to selectively share the information across the tasks. We assume that each task parameter vector is a linear combi- nation of a finite number of underlying basis tasks. The coefficients of the linear combina- tion are sparse in nature and the overlap in the sparsity patterns of two tasks controls the amount of sharing across these. Our model is based on on the assumption that task pa- rameters within a group lie in a low dimen- sional subspace but allows the tasks in differ- ent groups to overlap with each other in one or more bases. Experimental results on four datasets show that our approach outperforms competing methods.

Connected through hyperedge HE-1 (induced by source paper "federated continual learning with adaptive parameter communication"):
  with members:
    - "federated learning with matched averaging"
    - "scalable and order robust continual learning with additive parameter decomposition"
    - "communication efficient federated deep learning with asynchronous model update and temporally weighted aggregation"
    - "asynchronous online federated learning for edge devices"
    - "federated optimization for heterogeneous networks"
    - "efficient lifelong learning with a gem"
    - "reinforced continual learning"
    - "overcoming catastrophic forgetting with hard attention to the task"

Connected through hyperedge HE-2 (induced by source paper "unified multi domain learning and data imputation using adversarial autoencoder"):
  with members:
    - "gain missing data imputation using generative adversarial nets"
    - "handling incomplete heterogeneous data using vaes"
    - "evolutionary architecture search for deep multitask networks"
    - "taking a closer look at domain shift category level adversaries for semantics consistent domain adaptation"
    - "cycada cycle consistent adversarial domain adaptation"
    - "unified deep supervised domain adaptation and generalization"
    - "vae with a vampprior"
    - "a survey on multi task learning"

Connected through hyperedge HE-3 (induced by source paper "scalable and order robust continual learning with additive parameter decomposition"):
  with members:
    - "learn to grow a continual structure learning framework for overcoming catastrophic forgetting"
    - "efficient lifelong learning with a gem"
    - "reinforced continual learning"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "unsupervised representation learning by predicting image rotations"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "progressive neural networks"
    - "lifelong learning with dynamically expandable networks"

Connected through hyperedge HE-4 (induced by source paper "adversarial encoder multi task decoder for multi stage processes"):
  with members:
    - "multi label learning with missing labels using mixed dependency graphs"
    - "statistical parametric speech synthesis using generative adversarial networks under a multi task learning framework"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "a survey on multi task learning"
    - "an overview of multi task learning in deep neural networks"
    - "cnn rnn a unified framework for multi label image classification"
    - "semi supervised learning with deep generative models"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-5 (induced by source paper "deep multi task augmented feature learning via hierarchical graph neural network"):
  with members:
    - "graph star net for generalized multi task learning"
    - "multi task learning over graph structures"
    - "learning to multitask"
    - "deep multi task representation learning a tensor factorisation approach"
    - "trace norm regularised deep multi task learning"
    - "multi domain dialog state tracking using recurrent neural networks"
    - "heterogeneous multi task learning for human pose estimation with deep convolutional neural network"
    - "a convex formulation for learning task relationships in multi task learning"

Connected through hyperedge HE-6 (induced by source paper "deep multi task learning via generalized tensor trace norm"):
  with members:
    - "learning to multitask"
    - "deep multi task representation learning a tensor factorisation approach"
    - "trace norm regularised deep multi task learning"
    - "learning multiple tasks with multilinear relationship networks"
    - "very deep convolutional networks for large scale image recognition"
    - "a convex formulation for learning task relationships in multi task learning"

Connected through hyperedge HE-7 (induced by source paper "continual learning in neural networks"):
  with members:
    - "class incremental learning via deep model consolidation"
    - "knowledge transfer in deep block modular neural networks"
    - "meta learning representations for continual learning"
    - "continual reinforcement learning in 3d non stationary environments"
    - "continual learning with tiny episodic memories"
    - "continual classification learning using generative models"
    - "task free continual learning"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"

Connected through hyperedge HE-8 (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"

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)