human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "sticking to the facts confident decoding for faithful data to text generation"):
  Abstract: Neural conditional text generation systems have achieved significant progress in recent years, showing the ability to produce highly fluent text. However, the inherent lack of controllability in these systems allows them to hallucinate factually incorrect phrases that are unfaithful to the source, making them often unsuitable for many real world systems that require high degrees of precision. In this work, we propose a novel confidence oriented decoder that assigns a confidence score to each target position. This score is learned in training using a variational Bayes objective, and can be leveraged at inference time using a calibration technique to promote more faithful generation. Experiments on a structured data-to-text dataset -- WikiBio -- show that our approach is more faithful to the source than existing state-of-the-art approaches, according to both automatic metrics and human evaluation.

Includes members:
  Member "key fact as pivot a two stage model for low resource table to text generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "variational template machine for data to text generation")
  Member "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial filters of dataset biases")
  Member "data to text generation with entity modeling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a hierarchical model for data to text generation")
  Member "text generation with exemplar based adaptive decoding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "low resource response generation with template prior")
  Member "handling divergent reference texts when evaluating table to text generation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bootstrapping generators from noisy data")
  Member "calibration entropy rates and memory in language models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on a class of markov order estimators based on ppm and other universal codes")
  Member "leveraging pre trained checkpoints for sequence generation tasks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "xpersona evaluating multilingual personalized chatbot")
  Member "bottom up abstractive summarization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "minilm deep self attention distillation for task agnostic compression of pre trained transformers")

Question: Please tell me which class the source paper 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)