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: "transferable natural language interface to structured queries aided by adversarial generation"
  Abstract: A natural language interface (NLI) to structured query is intriguing due to its wide industrial applications and high economical values. In this work, we tackle the problem of domain adaptation for NLI with limited data on target domain. Two important approaches are considered: (a) effective general-knowledge-learning on source domain semantic parsing, and (b) data augmentation on target domain. We present a Structured Query Inference Network (SQIN) to enhance learning for domain adaptation, by separating schema information from NL and decoding SQL in a more structural-aware manner; we also propose a GAN-based augmentation technique (AugmentGAN) to mitigate the issue of lacking target domain data. We report solid results on GeoQuery, Overnight, and WikiSQL to demonstrate state-of-the-art performances for both in-domain and domain-transfer tasks.

Connected through hyperedge HE-1 (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"
    - "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"
    - "unified pragmatic models for generating and following instructions"

Connected through hyperedge HE-2 (induced by source paper "a sketch based system for semantic parsing"):
  with members:
    - "a comprehensive exploration on wikisql with table aware word contextualization"
    - "bert for joint intent classification and slot filling"
    - "sqlnet generating structured queries from natural language without reinforcement learning"
    - "semantic parsing with syntax and table aware sql generation"
    - "memory augmented policy optimization for program synthesis and semantic parsing"
    - "sequence to action end to end semantic graph generation for semantic parsing"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "coarse to fine decoding for neural semantic parsing"

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)