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: "natural language to structured query generation via meta learning"
  Abstract: In conventional supervised training, a model is trained to fit all the training examples. However, having a monolithic model may not always be the best strategy, as examples could vary widely. In this work, we explore a different learning protocol that treats each example as a unique pseudo-task, by reducing the original learning problem to a few-shot meta-learning scenario with the help of a domain-dependent relevance function. When evaluated on the WikiSQL dataset, our approach leads to faster convergence and achieves 1.1%--5.4% absolute accuracy gains over the non-meta-learning counterparts.

Connected through hyperedge HE-1 (induced by source paper "revisiting meta learning as supervised learning"):
  with members:
    - "feature critic networks for heterogeneous domain generalization"
    - "task2vec task embedding for meta learning"
    - "a closer look at few shot classification"
    - "the information complexity of learning tasks their structure and their distance"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "meta learning a survey"
    - "meta learning with latent embedding optimization"
    - "meta learning transferable active learning policies by deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "learning to generalize from sparse and underspecified rewards"):
  with members:
    - "self supervised generalisation with meta auxiliary learning"
    - "reward shaping via meta learning"
    - "on first order meta learning algorithms"
    - "multi task maximum entropy inverse reinforcement learning"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "discourse aware neural rewards for coherent text generation"
    - "babyai first steps towards grounded language learning with a human in the loop"

Connected through hyperedge HE-3 (induced by source paper "learning semantic parsers from denotations with latent structured alignments and abstract programs"):
  with members:
    - "learning to infer program sketches"
    - "learning to generalize from sparse and underspecified rewards"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "improving text to sql evaluation methodology"
    - "semantic parsing with syntax and table aware sql generation"
    - "allennlp a deep semantic natural language processing platform"
    - "decoupling structure and lexicon for zero shot semantic parsing"

Connected through hyperedge HE-4 (induced by source paper "actively seeking and learning from live data"):
  with members:
    - "on first order meta learning algorithms"
    - "deep nets what have they ever done for vision"
    - "deep learning a critical appraisal"
    - "overcoming language priors in visual question answering with adversarial regularization"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "an empirical evaluation of visual question answering for novel objects"
    - "visual question answering as a meta learning task"
    - "one shot visual imitation learning via meta learning"

Connected through hyperedge HE-5 (induced by source paper "neural semantic parsing in low resource settings with back translation and meta learning"):
  with members:
    - "learning to generalize from sparse and underspecified rewards"
    - "unsupervised neural machine translation with smt as posterior regularization"
    - "question generation from sql queries improves neural semantic parsing"
    - "building a neural semantic parser from a domain ontology"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "policy shaping and generalized update equations for semantic parsing from denotations"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "how to train your maml"

Connected through hyperedge HE-6 (induced by source paper "table to text natural language generation with unseen schemas"):
  with members:
    - "key fact as pivot a two stage model for low resource table to text generation"
    - "domain adaptive dialog generation via meta learning"
    - "domain adaptation of neural machine translation by lexicon induction"
    - "step by step separating planning from realization in neural data to text generation"
    - "learning to compose topic aware mixture of experts for zero shot video captioning"
    - "zero shot question generation from knowledge graphs for unseen predicates and entity types"
    - "data to text generation with content selection and planning"
    - "get to the point summarization with pointer generator networks"

Connected through hyperedge HE-7 (induced by source paper "enhanced meta learning for cross lingual named entity recognition with minimal resources"):
  with members:
    - "domain adaptive dialog generation via meta learning"
    - "beto bentz becas the surprising cross lingual effectiveness of bert"
    - "cross lingual language model pretraining"
    - "chinese ner using lattice lstm"
    - "meta learning for low resource neural machine translation"
    - "deep contextualized word representations"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "zero shot text to sql learning with auxiliary task"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "improving text to sql evaluation methodology"
    - "decoupling structure and lexicon for zero shot semantic parsing"
    - "incsql training incremental text to sql parsers with non deterministic oracles"
    - "typesql knowledge based type aware neural text to sql generation"
    - "coarse to fine decoding for neural semantic parsing"
    - "robust text to sql generation with execution guided decoding"

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.CL(Computation and Language)