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 text from structured data with application to the biography domain"
  Abstract: This paper introduces a neural model for concept-to-text generation that scales to large, rich domains. We experiment with a new dataset of biographies from Wikipedia that is an order of magni- tude larger than existing resources with over 700k samples. The dataset is also vastly more diverse with a 400k vocab- ulary, compared to a few hundred words for Weathergov or Robocup. Our model builds upon recent work on conditional neural language model for text genera- tion. To deal with the large vocabulary, we extend these models to mix a fixed vocabulary with copy actions that trans- fer sample-specific words from the in- put database to the generated output sen- tence. Our neural model significantly out- performs a classical Kneser-Ney language model adapted to this task by nearly 15 BLEU.

Connected through hyperedge HE-1 (induced by source paper "be concise and precise synthesizing open domain entity descriptions from facts"):
  with members:
    - "generating wikipedia by summarizing long sequences"
    - "neuralreg an end to end approach to referring expression generation"
    - "generating fine grained open vocabulary entity type descriptions"
    - "get to the point summarization with pointer generator networks"
    - "a neural knowledge language model"
    - "pointing the unknown words"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "video captioning with multi faceted attention"

Connected through hyperedge HE-2 (induced by source paper "generating fine grained open vocabulary entity type descriptions"):
  with members:
    - "generating wikipedia by summarizing long sequences"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "get to the point summarization with pointer generator networks"
    - "supervised typing of big graphs using semantic embeddings"
    - "challenges in data to document generation"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "video captioning with multi faceted attention"
    - "dynamic memory networks for visual and textual question answering"

Connected through hyperedge HE-3 (induced by source paper "multi lingual neural title generation for e commerce browse pages"):
  with members:
    - "learning to generate one sentence biographies from wikidata"
    - "six challenges for neural machine translation"
    - "generating factoid questions with recurrent neural networks the 30m factoid question answer corpus"
    - "transfer learning for low resource neural machine translation"
    - "multi source neural translation"
    - "google s multilingual neural machine translation system enabling zero shot translation"
    - "what to talk about and how selective generation using lstms with coarse to fine alignment"
    - "effective approaches to attention based neural machine translation"

Connected through hyperedge HE-4 (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"

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)