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 "description based text classification with reinforcement learning"):
  Abstract: The task of text classification is usually divided into two stages: {\it text feature extraction} and {\it classification}. In this standard formalization categories are merely represented as indexes in the label vocabulary, and the model lacks for explicit instructions on what to classify. Inspired by the current trend of formalizing NLP problems as question answering tasks, we propose a new framework for text classification, in which each category label is associated with a category description. Descriptions are generated by hand-crafted templates or using abstractive/extractive models from reinforcement learning. The concatenation of the description and the text is fed to the classifier to decide whether or not the current label should be assigned to the text. The proposed strategy forces the model to attend to the most salient texts with respect to the label, which can be regarded as a hard version of attention, leading to better performances. We observe significant performance boosts over strong baselines on a wide range of text classification tasks including single-label classification, multi-label classification and multi-aspect sentiment analysis.

Includes members:
  Member "exploring the limits of transfer learning with a unified text to text transformer":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on contextual embeddings")
  Member "utilizing bert for aspect based sentiment analysis via constructing auxiliary sentence":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "snippext semi supervised opinion mining with augmented data")
  Member "docbert bert for document classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pel bert a joint model for protocol entity linking")
  Member "question answering is a format when is it useful":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a framework for evaluation of machine reading comprehension gold standards")
  Member "entity relation extraction as multi turn question answering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deeper task specificity improves joint entity and relation extraction")
  Member "integrating semantic knowledge to tackle zero shot text classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "human grounded evaluations of explanation methods for text classification")
  Member "coreference resolution as query based span prediction":
    also connected through other hyperedges:
      (none)
  Member "putting the horse before the cart a generator evaluator framework for question generation from text":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reinforcement learning based graph to sequence model for natural question generation")

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)