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 to learn semantic parsers from natural language supervision"
  Abstract: As humans, we often rely on language to learn language. For example, when corrected in a conversation, we may learn from that correction, over time improving our language fluency. Inspired by this observation, we propose a learning algorithm for training semantic parsers from supervision (feedback) expressed in natural language. Our algorithm learns a semantic parser from users' corrections such as "no, what I really meant was before his job, not after", by also simultaneously learning to parse this natural language feedback in order to leverage it as a form of supervision. Unlike supervision with gold-standard logical forms, our method does not require the user to be familiar with the underlying logical formalism, and unlike supervision from denotation, it does not require the user to know the correct answer to their query. This makes our learning algorithm naturally scalable in settings where existing conversational logs are available and can be leveraged as training data. We construct a novel dataset of natural language feedback in a conversational setting, and show that our method is effective at learning a semantic parser from such natural language supervision.

Connected through hyperedge HE-1 (induced by source paper "don t paraphrase detect rapid and effective data collection for semantic parsing"):
  with members:
    - "gqa a new dataset for real world visual reasoning and compositional question answering"
    - "end to end open domain question answering with"
    - "practical semantic parsing for spoken language understanding"
    - "building a neural semantic parser from a domain ontology"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "improving text to sql evaluation methodology"
    - "building a conversational agent overnight with dialogue self play"
    - "allennlp a deep semantic natural language processing platform"

Connected through hyperedge HE-2 (induced by source paper "model based interactive semantic parsing a unified framework and a text to sql case study"):
  with members:
    - "a comprehensive exploration on wikisql with table aware word contextualization"
    - "towards complex text to sql in cross domain database with intermediate representation"
    - "representing schema structure with graph neural networks for text to sql parsing"
    - "quantifying uncertainties in natural language processing tasks"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sql to text generation with graph to sequence model"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "improving text to sql evaluation methodology"

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)