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 executable semantic parsers for natural language understanding"
  Abstract: For building question answering systems and natural language interfaces, semantic parsing has emerged as an important and powerful paradigm. Semantic parsers map natural language into logical forms, the classic representation for many important linguistic phenomena. The modern twist is that we are interested in learning semantic parsers from data, which introduces a new layer of statistical and computational issues. This article lays out the components of a statistical semantic parser, highlighting the key challenges. We will see that semantic parsing is a rich fusion of the logical and the statistical world, and that this fusion will play an integral role in the future of natural language understanding systems.

Connected through hyperedge HE-1 (induced by source paper "don t parse generate a sequence to sequence architecture for task oriented semantic parsing"):
  with members:
    - "improving semantic parsing for task oriented dialog"
    - "roberta a robustly optimized bert pretraining approach"
    - "joint slot filling and intent detection via capsule neural networks"
    - "exploring neural methods for parsing discourse representation structures"
    - "style transfer through back translation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "snips voice platform an embedded spoken language understanding system for private by design voice interfaces"

Connected through hyperedge HE-2 (induced by source paper "ecological semantics programming environments for situated language understanding"):
  with members:
    - "bringing stories alive generating interactive fiction worlds"
    - "environmental drivers of systematicity and generalization in a situated agent"
    - "algorithmic improvements for deep reinforcement learning applied to interactive fiction"
    - "extending machine language models toward human level language understanding"
    - "berts of a feather do not generalize together large variability in generalization across models with similar test set performance"
    - "human few shot learning of compositional instructions"
    - "do neural language representations learn physical commonsense"
    - "a survey of reinforcement learning informed by natural language"

Connected through hyperedge HE-3 (induced by source paper "why build an assistant in minecraft"):
  with members:
    - "craftassist a framework for dialogue enabled interactive agents"
    - "habitat a platform for embodied ai research"
    - "learning from dialogue after deployment feed yourself chatbot"
    - "babyai a platform to study the sample efficiency of grounded language learning"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "babyai first steps towards grounded language learning with a human in the loop"
    - "towards deep conversational recommendations"
    - "learning to ask questions in open domain conversational systems with typed decoders"

Connected through hyperedge HE-4 (induced by source paper "program synthesis and semantic parsing with learned code idioms"):
  with members:
    - "encoding database schemas with relation aware self attention for text to sql parsers"
    - "learning programmatic idioms for scalable semantic parsing"
    - "a grammar based structural cnn decoder for code generation"
    - "self attention with relative position representations"
    - "generative code modeling with graphs"
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "coarse to fine decoding for neural semantic parsing"
    - "spider a large scale human labeled dataset for complex and cross domain semantic parsing and text to sql task"

Connected through hyperedge HE-5 (induced by source paper "building an application independent natural language interface"):
  with members:
    - "spatialnli a spatial domain natural language interface to databases using spatial comprehension"
    - "bridging the semantic gap with sql query logs in natural language interfaces to databases"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "transferable natural language interface to structured queries aided by adversarial generation"
    - "nl2bash a corpus and semantic parser for natural language interface to the linux operating system"
    - "an end to end neural natural language interface for databases"
    - "explain yourself a natural language interface for scrutable autonomous robots"
    - "mapping natural language commands to web elements"

Connected through hyperedge HE-6 (induced by source paper "a hybrid semantic parsing approach for tabular data analysis"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "the natural language decathlon multitask learning as question answering"
    - "semantic parsing with syntax and table aware sql generation"
    - "typesql knowledge based type aware neural text to sql generation"
    - "learning to compute word embeddings on the fly"
    - "universal semantic parsing"
    - "mimicking word embeddings using subword rnns"

Connected through hyperedge HE-7 (induced by source paper "context dependent semantic parsing over temporally structured data"):
  with members:
    - "learning to map context dependent sentences to executable formal queries"
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "efficient summarization with read again and copy mechanism"
    - "a deep reinforced model for abstractive summarization"
    - "pointing the unknown words"
    - "sequence to sequence learning as beam search optimization"
    - "language to logical form with neural attention"
    - "incorporating copying mechanism in sequence to sequence learning"

Connected through hyperedge HE-8 (induced by source paper "bridging the semantic gap with sql query logs in natural language interfaces to databases"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "an end to end neural natural language interface for databases"
    - "spider a large scale human labeled dataset for complex and cross domain semantic parsing and text to sql task"
    - "dataset for a neural natural language interface for databases nnlidb"
    - "learning a neural semantic parser from user feedback"
    - "language to logical form with neural attention"
    - "a joint many task model growing a neural network for multiple nlp tasks"

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)