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: "select and attend towards controllable content selection in text generation"
  Abstract: Many text generation tasks naturally contain two steps: content selection and surface realization. Current neural encoder-decoder models conflate both steps into a black-box architecture. As a result, the content to be described in the text cannot be explicitly controlled. This paper tackles this problem by decoupling content selection from the decoder. The decoupled content selection is human interpretable, whose value can be manually manipulated to control the content of generated text. The model can be trained end-to-end without human annotations by maximizing a lower bound of the marginal likelihood. We further propose an effective way to trade-off between performance and controllability with a single adjustable hyperparameter. In both data-to-text and headline generation tasks, our model achieves promising results, paving the way for controllable content selection in text generation.

Connected through hyperedge HE-1 (induced by source paper "unsupervised pidgin text generation by pivoting english data and self training"):
  with members:
    - "zero shot paraphrase generation with multilingual language models"
    - "revisiting self training for neural sequence generation"
    - "pidginunmt unsupervised neural machine translation from west african pidgin to english"
    - "neobility at semeval 2017 task 1 an attention based sentence similarity model"
    - "unsupervised machine translation using monolingual corpora only"
    - "unsupervised neural machine translation"
    - "neural text generation from structured data with application to the biography domain"

Connected through hyperedge HE-2 (induced by source paper "personalized patent claim generation and measurement"):
  with members:
    - "text generation from knowledge graphs with graph transformers"
    - "spanbert improving pre training by representing and predicting spans"
    - "patentbert patent classification with fine tuning a pre trained bert model"
    - "scibert a pretrained language model for scientific text"
    - "docbert bert for document classification"
    - "mass masked sequence to sequence pre training for language generation"
    - "the evolved transformer"
    - "defending against neural fake news"

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)