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 "discourse based objectives for fast unsupervised sentence representation learning"):
  Abstract: This work presents a novel objective function for the unsupervised training of neural network sentence encoders. It exploits signals from paragraph-level discourse coherence to train these models to understand text. Our objective is purely discriminative, allowing us to train models many times faster than was possible under prior methods, and it yields models which perform well in extrinsic evaluations.

Includes members:
  Member "a latent variable recurrent neural network for discourse relation language models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "modelling latent skills for multitask language generation")
  Member "unsupervised pretraining for sequence to sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on contextual embeddings")
  Member "embracing data abundance booktest dataset for reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "investigating prior knowledge for challenging chinese machine reading comprehension")
  Member "neural net models for open domain discourse coherence":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximating interactive human evaluation with self play for open domain dialog systems")
  Member "learning distributed representations of sentences from unlabelled data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "contextual lensing of universal sentence representations")
  Member "a neural attention model for abstractive sentence summarization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a 3 accelerating attention mechanisms in neural networks with approximation")
  Member "document context language models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "transformer xl attentive language models beyond a fixed length context")
  Member "skip thought vectors":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey on contextual embeddings")

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)