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: "bayesian multitask learning with latent hierarchies"
  Abstract: We learn multiple hypotheses for related tasks under a latent hierarchical relationship between tasks. We exploit the intuition that for domain adaptation, we wish to share classifier structure, but for multitask learning, we wish to share covariance structure. Our hierarchical model is seen to subsume several previously proposed multitask learning models and performs well on three distinct real-world data sets.

Connected through hyperedge HE-1 (induced by source paper "learning task relatedness in multi task learning for images in context"):
  with members:
    - "taskonomy disentangling task transfer learning"
    - "gnas a greedy neural architecture search method for multi attribute learning"
    - "sketchparse towards rich descriptions for poorly drawn sketches using multi task hierarchical deep networks"
    - "an overview of multi task learning in deep neural networks"
    - "omniart multi task deep learning for artistic data analysis"
    - "deep cross residual learning for multitask visual recognition"
    - "distributed multi task relationship learning"
    - "deep multi task representation learning a tensor factorisation approach"

Connected through hyperedge HE-2 (induced by source paper "multitask hopfield networks"):
  with members:
    - "clustered multi task learning a convex formulation"

Connected through hyperedge HE-3 (induced by source paper "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"):
  with members:
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "domain adaptation for visual applications a comprehensive survey"
    - "robust imitation of diverse behaviors"
    - "learning from synthetic humans"
    - "emergence of locomotion behaviours in rich environments"
    - "how useful is photo realistic rendering for visual learning"
    - "unrealcv connecting computer vision to unreal engine"
    - "semantic scene completion from a single depth image"

Connected through hyperedge HE-4 (induced by source paper "online gradient based mixtures for transfer modulation in meta learning"):
  with members:
    - "recasting gradient based meta learning as hierarchical bayes"
    - "meta learning with latent embedding optimization"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "probabilistic model agnostic meta learning"
    - "metalearning with hebbian fast weights"
    - "the effects of negative adaptation in model agnostic meta learning"
    - "dynamic few shot visual learning without forgetting"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-5 (induced by source paper "multi task learning of pairwise sequence classification tasks over disparate label spaces"):
  with members:
    - "the repeval 2017 shared task multi genre natural language inference with sentence representations"
    - "a simple but tough to beat baseline for the fake news challenge stance detection task"
    - "semi supervised multitask learning for sequence labeling"
    - "deep multitask learning for semantic dependency parsing"
    - "learning to select data for transfer learning with bayesian optimization"
    - "recurrent neural network based sentence encoder with gated attention for natural language inference"
    - "emoji2vec learning emoji representations from their description"
    - "multilingual part of speech tagging with bidirectional long short term memory models and auxiliary loss"

Connected through hyperedge HE-6 (induced by source paper "probabilistic model agnostic meta learning"):
  with members:
    - "on first order meta learning algorithms"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "reptile a scalable metalearning algorithm"
    - "one shot visual imitation learning via meta learning"
    - "prototypical networks for few shot learning"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "meta learning and universality deep representations and gradient descent can approximate any learning algorithm"
    - "meta sgd learning to learn quickly for few shot learning"

Connected through hyperedge HE-7 (induced by source paper "recasting gradient based meta learning as hierarchical bayes"):
  with members:
    - "prototypical networks for few shot learning"
    - "learning to compare relation network for few shot learning"
    - "a simple neural attentive meta learner"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "few shot learning through an information retrieval lens"
    - "matching networks for one shot learning"
    - "building machines that learn and think like people"
    - "low shot visual object recognition"

Connected through hyperedge HE-8 (induced by source paper "bayesian model agnostic meta learning"):
  with members:
    - "recasting gradient based meta learning as hierarchical bayes"
    - "prototypical networks for few shot learning"
    - "stein variational policy gradient"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "matching networks for one shot learning"
    - "vime variational information maximizing exploration"
    - "bayesian dark knowledge"

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)