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: "bootstrapping generators from noisy data"
  Abstract: A core step in statistical data-to-text generation concerns learning correspondences between structured data representations (e.g., facts in a database) and associated texts. In this paper we aim to bootstrap generators from large scale datasets where the data (e.g., DBPedia facts) and related texts (e.g., Wikipedia abstracts) are loosely aligned. We tackle this challenging task by introducing a special-purpose content selection mechanism. We use multi-instance learning to automatically discover correspondences between data and text pairs and show how these can be used to enhance the content signal while training an encoder-decoder architecture. Experimental results demonstrate that models trained with content-specific objectives improve upon a vanilla encoder-decoder which solely relies on soft attention.

Connected through hyperedge HE-1 (induced by source paper "latent relation language models"):
  with members:
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "transformer xl attentive language models beyond a fixed length context"
    - "neural lattice language models"
    - "describing a knowledge base"
    - "deep contextualized word representations"
    - "building language models for text with named entities"
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "learning to compute word embeddings on the fly"

Connected through hyperedge HE-2 (induced by source paper "generating summaries with topic templates and structured convolutional decoders"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "generating wikipedia by summarizing long sequences"
    - "don t give me the details just the summary topic aware convolutional neural networks for extreme summarization"
    - "deep graph convolutional encoders for structured data to text generation"
    - "data to text generation with content selection and planning"
    - "attention is all you need"
    - "get to the point summarization with pointer generator networks"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-3 (induced by source paper "data to text generation with entity modeling"):
  with members:
    - "data to text generation with content selection and planning"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "challenges in data to document generation"
    - "dynamic entity representations in neural language models"
    - "pointing the unknown words"
    - "neural text generation from structured data with application to the biography domain"
    - "tracking the world state with recurrent entity networks"
    - "what to talk about and how selective generation using lstms with coarse to fine alignment"

Connected through hyperedge HE-4 (induced by source paper "data to text generation with content selection and planning"):
  with members:
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "opennmt open source toolkit for neural machine translation"
    - "learning to generate one sentence biographies from wikidata"
    - "best worst scaling more reliable than rating scales a case study on sentiment intensity annotation"
    - "order planning neural text generation from structured data"
    - "table to text generation by structure aware seq2seq learning"
    - "pointing the unknown words"
    - "incorporating copying mechanism in sequence to sequence 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)