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: "acquiring correct knowledge for natural language generation"
  Abstract: Natural language generation (NLG) systems are computer software systems that produce texts in English and other human languages, often from non-linguistic input data. NLG systems, like most ai systems, need substantial amounts of knowledge. However, our experience in two NLG projects suggests that it is difficult to acquire correct knowledge for NLG systems; indeed, every knowledge acquisition (KA) technique we tried had significant problems. In general terms, these problems were due to the complexity, novelty, and poorly understood nature of the tasks our systems attempted, and were worsened by the fact that people write so differently. This meant in particular that corpus-based KA approaches suffered because it was impossible to assemble a sizable corpus of high-quality consistent manually written texts in our domains; and structured expert-oriented KA techniques suffered because experts disagreed and because we could not get enough information about special and unusual cases to build robust systems. We believe that such problems are likely to affect many other NLG systems as well. In the long term, we hope that new KA techniques may emerge to help NLG system builders. In the shorter term, we believe that understanding how individual KA techniques can fail, and using a mixture of different KA techniques with different strengths and weaknesses, can help developers acquire NLG knowledge that is mostly correct.

Connected through hyperedge HE-1 (induced by source paper "words of estimative correlation studying verbalizations of scatterplots"):
  with members:
    - "exploranative code quality documents"
    - "noun phrase co occurrence statistics for semi automatic semantic lexicon construction"

Connected through hyperedge HE-2 (induced by source paper "extracting linguistic resources from the web for concept to text generation"):
  with members:
    - "generating natural language descriptions from owl ontologies the natural owl system"
    - "a survey of paraphrasing and textual entailment methods"
    - "trainable methods for surface natural language generation"

Connected through hyperedge HE-3 (induced by source paper "on modeling vagueness and uncertainty in data to text systems through fuzzy sets"):
  with members:
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "an empirical approach for modeling fuzzy geographical descriptors"
    - "linguistic descriptions for automatic generation of textual short term weather forecasts on real prediction data"

Connected through hyperedge HE-4 (induced by source paper "content selection in data to text systems a survey"):
  with members:
    - "natural language generation enhances human decision making with uncertain information"
    - "linguistic descriptions for automatic generation of textual short term weather forecasts on real prediction data"
    - "individual and domain adaptation in sentence planning for dialogue"
    - "catching the drift probabilistic content models with applications to generation and summarization"

Connected through hyperedge HE-5 (induced by source paper "generating weather forecast texts with case based reasoning"):
  with members:
    - "learning to order facts for discourse planning in natural language generation"

Connected through hyperedge HE-6 (induced by source paper "is it morally acceptable for a system to lie to persuade me"):
  with members:
    - "ecological evaluation of persuasive messages using google adwords"

Connected through hyperedge HE-7 (induced by source paper "individual and domain adaptation in sentence planning for dialogue"):
  with members:
    - "learning content selection rules for generating object descriptions in dialogue"
    - "a flexible pragmatics driven language generator for animated agents"
    - "bootstrapping lexical choice via multiple sequence alignment"
    - "inferring strategies for sentence ordering in multidocument news summarization"
    - "cue phrase classification using machine learning"

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)