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 "is artificial data useful for biomedical natural language processing algorithms"):
  Abstract: A major obstacle to the development of Natural Language Processing (NLP) methods in the biomedical domain is data accessibility. This problem can be addressed by generating medical data artificially. Most previous studies have focused on the generation of short clinical text, and evaluation of the data utility has been limited. We propose a generic methodology to guide the generation of clinical text with key phrases. We use the artificial data as additional training data in two key biomedical NLP tasks: text classification and temporal relation extraction. We show that artificially generated training data used in conjunction with real training data can lead to performance boosts for data-greedy neural network algorithms. We also demonstrate the usefulness of the generated data for NLP setups where it fully replaces real training data.

Includes members:
  Member "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned":
    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 "investigating backtranslation in neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "do all roads lead to rome understanding the role of initialization in iterative back translation")
  Member "natural language generation for electronic health records":
    also connected through other hyperedges:
      (none)
  Member "on the automatic generation of medical imaging reports":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "learning to write notes in electronic health records":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fast prototyping a dialogue comprehension system for nurse patient conversations on symptom monitoring")
  Member "learning neural templates for text generation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "paraphrase generation with latent bag of words")
  Member "hierarchical neural story generation":
    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 "understanding back translation at scale":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on feature normalization and data augmentation")

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)