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 "how decoding strategies affect the verifiability of generated text"):
  Abstract: Language models are of considerable importance. They are used for pretraining, finetuning, and rescoring in downstream applications, and as is as a test-bed and benchmark for progress in natural language understanding. One fundamental question regards the way we should generate text from a language model. It is well known that different decoding strategies can have dramatic impact on the quality of the generated text and using the most likely sequence under the model distribution, e.g., via beam search, generally leads to degenerate and repetitive outputs. #R##N#While generation strategies such as top-k and nucleus sampling lead to more natural and less repetitive generations, the true cost of avoiding the highest scoring solution is hard to quantify. In this paper, we argue that verifiability, i.e., the consistency of the generated text with factual knowledge, is a suitable metric for measuring this cost. We use an automatic fact-checking system to calculate new metrics as a function of the number of supported claims per sentence and find that sampling-based generation strategies, such as top-k, indeed lead to less verifiable text. This finding holds across various dimensions, such as model size, training data size and parameters of the generation strategy. Based on this finding, we introduce a simple and effective generation strategy for producing non-repetitive and more verifiable (in comparison to other methods) text.

Includes members:
  Member "fairseq a fast extensible toolkit for sequence modeling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")
  Member "language models as knowledge bases":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "natural language qa approaches using reasoning with external knowledge")
  Member "investigating entity knowledge in bert with simple neural end to end entity linking":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pel bert a joint model for protocol entity linking")
  Member "mixture content selection for diverse sequence generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sampling good latent variables via cpp vaes vaes with condition posterior as prior")
  Member "barack s wife hillary using knowledge graphs for fact aware language modeling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "enteval a holistic evaluation benchmark for entity representations")
  Member "the curious case of neural text degeneration":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an empirical investigation of pre trained transformer language models for open domain dialogue generation")
  Member "tmlab generative enhanced model gem for adversarial attacks":
    also connected through other hyperedges:
      (none)
  Member "real or fake learning to discriminate machine from human generated text":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "limits of detecting text generated by large scale language models")

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)