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 "reverse transfer learning can word embeddings trained for different nlp tasks improve neural language models"):
  Abstract: Natural language processing (NLP) tasks tend to suffer from a paucity of suitably annotated training data, hence the recent success of transfer learning across a wide variety of them. The typical recipe involves: (i) training a deep, possibly bidirectional, neural network with an objective related to language modeling, for which training data is plentiful; and (ii) using the trained network to derive contextual representations that are far richer than standard linear word embeddings such as word2vec, and thus result in important gains. In this work, we wonder whether the opposite perspective is also true: can contextual representations trained for different NLP tasks improve language modeling itself? Since language models (LMs) are predominantly locally optimized, other NLP tasks may help them make better predictions based on the entire semantic fabric of a document. We test the performance of several types of pre-trained embeddings in neural LMs, and we investigate whether it is possible to make the LM more aware of global semantic information through embeddings pre-trained with a domain classification model. Initial experiments suggest that as long as the proper objective criterion is used during training, pre-trained embeddings are likely to be beneficial for neural language modeling.

Includes members:
  Member "deep contextualized word representations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "universal language model fine tuning for text classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "description based text classification with reinforcement learning")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "semi supervised multitask learning for sequence labeling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "exploring self supervised regularization for supervised and semi supervised learning")
  Member "relevance based word embedding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "selective weak supervision for neural information retrieval")
  Member "low rank rnn adaptation for context aware language modeling":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "c textgen conditional text generation for harmonious human machine interaction")
  Member "topicrnn a recurrent neural network with long range semantic dependency":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "variational hetero encoder randomized gans for joint image text modeling")

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)