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: "meta multi task learning for sequence modeling"
  Abstract: Semantic composition functions have been playing a pivotal role in neural representation learning of text sequences. In spite of their success, most existing models suffer from the underfitting problem: they use the same shared compositional function on all the positions in the sequence, thereby lacking expressive power due to incapacity to capture the richness of compositionality. Besides, the composition functions of different tasks are independent and learned from scratch. In this paper, we propose a new sharing scheme of composition function across multiple tasks. Specifically, we use a shared meta-network to capture the meta-knowledge of semantic composition and generate the parameters of the task-specific semantic composition models. We conduct extensive experiments on two types of tasks, text classification and sequence tagging, which demonstrate the benefits of our approach. Besides, we show that the shared meta-knowledge learned by our proposed model can be regarded as off-the-shelf knowledge and easily transferred to new tasks.

Connected through hyperedge HE-1 (induced by source paper "learning to few shot learn across diverse natural language classification tasks"):
  with members:
    - "parameter efficient transfer learning for nlp"
    - "bert rediscovers the classical nlp pipeline"
    - "meta learning for low resource natural language generation in task oriented dialogue systems"
    - "learning and evaluating general linguistic intelligence"
    - "to tune or not to tune adapting pretrained representations to diverse tasks"
    - "multi task deep neural networks for natural language understanding"
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "meta dataset a dataset of datasets for learning to learn from few examples"

Connected through hyperedge HE-2 (induced by source paper "unsupervised transfer learning via bert neuron selection"):
  with members:
    - "visualizing and measuring the geometry of bert"
    - "bert rediscovers the classical nlp pipeline"
    - "open sesame getting inside bert s linguistic knowledge"
    - "revealing the dark secrets of bert"
    - "linguistic knowledge and transferability of contextual representations"
    - "what is one grain of sand in the desert analyzing individual neurons in deep nlp models"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "dynamic meta embeddings for improved sentence representations"

Connected through hyperedge HE-3 (induced by source paper "federated multi task hierarchical attention model for sensor analytics"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "correlated time series forecasting using deep neural networks a summary of results"
    - "federated multi task learning"
    - "recognizing detailed human context in the wild from smartphones and smartwatches"
    - "communication efficient learning of deep networks from decentralized data"
    - "federated optimization distributed optimization beyond the datacenter"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-4 (induced by source paper "multi task deep morphological analyzer context aware joint morphological tagging and lemma prediction"):
  with members:
    - "neural machine translation for bilingually scarce scenarios a deep multi task learning approach"
    - "scheduled multi task learning from syntax to translation"
    - "online and linear time attention by enforcing monotonic alignments"
    - "cross lingual character level neural morphological tagging"
    - "improving generalization performance by switching from adam to sgd"
    - "recent trends in deep learning based natural language processing"
    - "exploiting linguistic resources for neural machine translation using multi task learning"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-5 (induced by source paper "meta learning multi task communication"):
  with members:
    - "taskonomy disentangling task transfer learning"
    - "exploring shared structures and hierarchies for multiple nlp tasks"
    - "meta learning for low resource neural machine translation"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "image aesthetic assessment an experimental survey"
    - "learning to generalize meta learning for domain generalization"
    - "adversarial multi task learning for text classification"
    - "model agnostic meta learning for fast adaptation of deep networks"

Connected through hyperedge HE-6 (induced by source paper "exploring shared structures and hierarchies for multiple nlp tasks"):
  with members:
    - "efficient neural architecture search via parameter sharing"
    - "routing networks adaptive selection of non linear functions for multi task learning"
    - "hierarchical representations for efficient architecture search"
    - "beyond shared hierarchies deep multitask learning through soft layer ordering"
    - "fully adaptive feature sharing in multi task networks with applications in person attribute classification"
    - "cross stitch networks for multi task learning"
    - "a joint many task model growing a neural network for multiple nlp tasks"
    - "reward augmented maximum likelihood for neural structured prediction"

Connected through hyperedge HE-7 (induced by source paper "multi task learning with multi view attention for answer selection and knowledge base question answering"):
  with members:
    - "modeling semantics with gated graph neural networks for knowledge base question answering"
    - "soft layer specific multi task summarization with entailment and question generation"
    - "same representation different attentions shareable sentence representation learning from multiple tasks"
    - "improved neural relation detection for knowledge base question answering"
    - "learning to rank question answer pairs with holographic dual lstm architecture"
    - "cross temporal recurrent networks for ranking question answer pairs"
    - "attentive pooling networks"
    - "squad 100 000 questions for machine comprehension of text"

Connected through hyperedge HE-8 (induced by source paper "girnet interleaved multi task recurrent state sequence models"):
  with members:
    - "exploring shared structures and hierarchies for multiple nlp tasks"
    - "sentiment analysis of code mixed indian languages an overview of sail_code mixed shared task icon 2017"
    - "adversarial multi task learning for text classification"
    - "an overview of multi task learning in deep neural networks"
    - "aspect level sentiment classification with deep memory network"
    - "recurrent neural network for text classification with multi task learning"
    - "neural architectures for named entity recognition"
    - "effective lstms for target dependent sentiment classification"

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.AI(Artificial Intelligence)