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 "using dynamic embeddings to improve static embeddings"):
  Abstract: How to build high-quality word embeddings is a fundamental research question in the field of natural language processing. Traditional methods such as Skip-Gram and Continuous Bag-of-Words learn {\it static} embeddings by training lookup tables that translate words into dense vectors. Static embeddings are directly useful for solving lexical semantics tasks, and can be used as input representations for downstream problems. Recently, contextualized embeddings such as BERT have been shown more effective than static embeddings as NLP input embeddings. Such embeddings are {\it dynamic}, calculated according to a sentential context using a network structure. One limitation of dynamic embeddings, however, is that they cannot be used without a sentence-level context. We explore the advantages of dynamic embeddings for training static embeddings, by using contextualized embeddings to facilitate training of static embedding lookup tables. Results show that the resulting embeddings outperform existing static embedding methods on various lexical semantics tasks.

Includes members:
  Member "visualizing and measuring the geometry of bert":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on contextual embeddings")
  Member "transformer xl attentive language models beyond a fixed length context":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on contextual embeddings")
  Member "simple bert models for relation extraction and semantic role labeling":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fine grained video text retrieval with hierarchical graph reasoning")
  Member "xlnet generalized autoregressive pretraining for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on contextual embeddings")
  Member "bert post training for review reading comprehension and aspect based sentiment analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "snippext semi supervised opinion mining with augmented data")
  Member "linguistic knowledge and transferability of contextual representations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on contextual embeddings")
  Member "roberta a robustly optimized bert pretraining approach":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "design challenges and misconceptions in neural sequence labeling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "hybrid neural tagging model for open relation extraction")

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)