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 can we know what language models know"):
  Abstract: Recent work has presented intriguing results examining the knowledge contained in language models (LM) by having the LM fill in the blanks of prompts such as "Obama is a _ by profession". These prompts are usually manually created, and quite possibly sub-optimal; another prompt such as "Obama worked as a _" may result in more accurately predicting the correct profession. Because of this, given an inappropriate prompt, we might fail to retrieve facts that the LM does know, and thus any given prompt only provides a lower bound estimate of the knowledge contained in an LM. In this paper, we attempt to more accurately estimate the knowledge contained in LMs by automatically discovering better prompts to use in this querying process. Specifically, we propose mining-based and paraphrasing-based methods to automatically generate high-quality and diverse prompts and ensemble methods to combine answers from different prompts. Extensive experiments on the LAMA benchmark for extracting relational knowledge from LMs demonstrate that our methods can improve accuracy from 31.1% to 38.1%, providing a tighter lower bound on what LMs know. We have released the code and the resulting LM Prompt And Query Archive (LPAQA) at this https URL.

Includes members:
  Member "facebook fair s wmt19 news translation task submission":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "covost a diverse multilingual speech to text translation corpus")
  Member "assessing bert s syntactic abilities":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "are pre trained language models aware of phrases simple but strong baselines for grammar induction")
  Member "bert rediscovers the classical nlp pipeline":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on contextual embeddings")
  Member "bert is not a knowledge base yet factual knowledge vs name based reasoning in unsupervised qa":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "k adapter infusing knowledge into pre trained models with adapters")
  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 "mask predict parallel decoding of conditional masked language models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "blank language models")
  Member "matching the blanks distributional similarity for relation learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "k adapter infusing knowledge into pre trained models with adapters")
  Member "latent relation language models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "kepler a unified model for knowledge embedding and pre trained language representation")

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)