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: "generating natural language descriptions from owl ontologies the natural owl system"
  Abstract: We present Naturalowl, a natural language generation system that produces texts describing individuals or classes of owl ontologies. Unlike simpler owl verbalizers, which typically express a single axiom at a time in controlled, often not entirely fluent natural language primarily for the benefit of domain experts, we aim to generate fluent and coherent multi-sentence texts for end-users. With a system like Naturalowl, one can publish information in owl on the Web, along with automatically produced corresponding texts in multiple languages, making the information accessible not only to computer programs and domain experts, but also end-users. We discuss the processing stages of Naturalowl, the optional domain-dependent linguistic resources that the system can use at each stage, and why they are useful. We also present trials showing that when the domain-dependent linguistic resources are available, Naturalowl produces significantly better texts compared to a simpler verbalizer, and that the resources can be created with relatively light effort.

Connected through hyperedge HE-1 (induced by source paper "a holistic natural language generation framework for the semantic web"):
  with members:
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"

Connected through hyperedge HE-2 (induced by source paper "table to text describing table region with natural language"):
  with members:
    - "challenges in data to document generation"
    - "pointing the unknown words"
    - "deep reinforcement learning for dialogue generation"
    - "generating factoid questions with recurrent neural networks the 30m factoid question answer corpus"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "interactive attention for neural machine translation"
    - "neural text generation from structured data with application to the biography domain"
    - "modeling coverage for neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "a mixed hierarchical attention based encoder decoder approach for standard table summarization"):
  with members:
    - "challenges in data to document generation"
    - "neural text generation from structured data with application to the biography domain"
    - "image to markup generation with coarse to fine attention"
    - "sequence to sequence rnns for text summarization"
    - "what to talk about and how selective generation using lstms with coarse to fine alignment"
    - "describing multimedia content using attention based encoder decoder networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "extracting linguistic resources from the web for concept to text generation"):
  with members:
    - "a survey of paraphrasing and textual entailment methods"
    - "acquiring correct knowledge for natural language generation"
    - "trainable methods for surface natural language generation"

Connected through hyperedge HE-5 (induced by source paper "survey of the state of the art in natural language generation core tasks applications and evaluation"):
  with members:
    - "toward controlled generation of text"
    - "controlling linguistic style aspects in neural language generation"
    - "affect lm a neural language model for customizable affective text generation"
    - "affective neural response generation"
    - "really well apparently bootstrapping improves the performance of sarcasm and nastiness classifiers for online dialogue"
    - "a persona based neural conversation model"
    - "write a classifier predicting visual classifiers from unstructured text descriptions"
    - "query focused opinion summarization for user generated content"

Connected through hyperedge HE-6 (induced by source paper "redundancy free verbalization of individuals for ontology validation"):
  with members:
    - "reasoning with individuals for the description logic shiq"

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)