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: "flexible modeling of latent task structures in multitask learning"
  Abstract: Multitask learning algorithms are typically designed assuming some fixed, a priori known latent structure shared by all the tasks. However, it is usually unclear what type of latent task structure is the most appropriate for a given multitask learning problem. Ideally, the "right" latent task structure should be learned in a data-driven manner. We present a flexible, nonparametric Bayesian model that posits a mixture of factor analyzers structure on the tasks. The nonparametric aspect makes the model expressive enough to subsume many existing models of latent task structures (e.g, mean-regularized tasks, clustered tasks, low-rank or linear/non-linear subspace assumption on tasks, etc.). Moreover, it can also learn more general task structures, addressing the shortcomings of such models. We present a variational inference algorithm for our model. Experimental results on synthetic and real-world datasets, on both regression and classification problems, demonstrate the effectiveness of the proposed method.

Connected through hyperedge HE-1 (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-2 (induced by source paper "multi task networks with universe group and task feature learning"):
  with members:
    - "soft layer specific multi task summarization with entailment and question generation"
    - "transfer and multi task learning for noun noun compound interpretation"
    - "aspect augmented adversarial networks for domain adaptation"
    - "adversarial multi task learning for text classification"
    - "transfer learning for neural semantic parsing"
    - "multi task label embedding for text classification"
    - "recurrent neural network for text classification with multi task learning"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-3 (induced by source paper "on the relationship between multitask neural networks and multitask gaussian processes"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "a survey on multi task learning"
    - "an overview of multi task learning in deep neural networks"
    - "steps toward deep kernel methods from infinite neural networks"

Connected through hyperedge HE-4 (induced by source paper "joint manifold diffusion for combining predictions on decoupled observations"):
  with members:
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "very deep convolutional networks for large scale image recognition"
    - "decaf a deep convolutional activation feature for generic visual recognition"
    - "algorithms for learning kernels based on centered alignment"
    - "learning task grouping and overlap in multi task learning"
    - "convex multitask learning with flexible task clusters"

Connected through hyperedge HE-5 (induced by source paper "spottune transfer learning through adaptive fine tuning"):
  with members:
    - "do better imagenet models transfer better"
    - "co regularized alignment for unsupervised domain adaptation"
    - "piggyback adapting a single network to multiple tasks by learning to mask weights"
    - "explicit inductive bias for transfer learning with convolutional networks"
    - "glomo unsupervisedly learned relational graphs as transferable representations"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "network dissection quantifying interpretability of deep visual representations"
    - "convolutional neural networks for medical image analysis full training or fine tuning"

Connected through hyperedge HE-6 (induced by source paper "a survey on multi task learning"):
  with members:
    - "learning to multitask"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "deep multi task representation learning a tensor factorisation approach"
    - "trace norm regularised deep multi task learning"
    - "modular multitask reinforcement learning with policy sketches"
    - "learning multiple visual tasks while discovering their structure"
    - "actor mimic deep multitask and transfer reinforcement learning"
    - "safe screening for multi task feature learning with multiple data matrices"

Connected through hyperedge HE-7 (induced by source paper "fully adaptive feature sharing in multi task networks with applications in person attribute classification"):
  with members:
    - "deep cross residual learning for multitask visual recognition"
    - "ubernet training a universal convolutional neural network for low mid and high level vision using diverse datasets and limited memory"
    - "cross stitch networks for multi task learning"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "deep attributes driven multi camera person re identification"
    - "walk and learn facial attribute representation learning from egocentric video and contextual data"
    - "learning deep representation for face alignment with auxiliary attributes"
    - "network of experts for large scale image categorization"

Connected through hyperedge HE-8 (induced by source paper "deep multi task representation learning a tensor factorisation approach"):
  with members:
    - "tensorizing neural networks"
    - "learning task grouping and overlap in multi task learning"
    - "frustratingly easy domain adaptation"
    - "clustered multi task learning a convex formulation"

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)