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: "dataset for a neural natural language interface for databases nnlidb"
  Abstract: Progress in natural language interfaces to databases (NLIDB) has been slow mainly due to linguistic issues (such as language ambiguity) and domain portability. Moreover, the lack of a large corpus to be used as a standard benchmark has made data-driven approaches difficult to develop and compare. In this paper, we revisit the problem of NLIDBs and recast it as a sequence translation problem. To this end, we introduce a large dataset extracted from the Stack Exchange Data Explorer website, which can be used for training neural natural language interfaces for databases. We also report encouraging baseline results on a smaller manually annotated test corpus, obtained using an attention-based sequence-to-sequence neural network.

Connected through hyperedge HE-1 (induced by source paper "spatialnli a spatial domain natural language interface to databases using spatial comprehension"):
  with members:
    - "seq2sql generating structured queries from natural language using reinforcement learning"
    - "encoding spatial relations from natural language"
    - "improving text to sql evaluation methodology"
    - "a transfer learnable natural language interface for databases"
    - "tranx a transition based neural abstract syntax parser for semantic parsing and code generation"
    - "neural semantic parsing over multiple knowledge bases"
    - "learning a neural semantic parser from user feedback"
    - "abstract syntax networks for code generation and semantic parsing"

Connected through hyperedge HE-2 (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"
    - "learning a neural semantic parser from user feedback"
    - "language to logical form with neural attention"
    - "learning executable semantic parsers for natural language understanding"
    - "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)