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 "multi domain neural network language generation for spoken dialogue systems"):
  Abstract: Moving from limited-domain natural language generation (NLG) to open domain is difficult because the number of semantic input combinations grows exponentially with the number of domains. Therefore, it is important to leverage existing resources and exploit similarities between domains to facilitate domain adaptation. In this paper, we propose a procedure to train multi-domain, Recurrent Neural Network-based (RNN) language generators via multiple adaptation steps. In this procedure, a model is first trained on counterfeited data synthesised from an out-of-domain dataset, and then fine tuned on a small set of in-domain utterances with a discriminative objective function. Corpus-based evaluation results show that the proposed procedure can achieve competitive performance in terms of BLEU score and slot error rate while significantly reducing the data needed to train generators in new, unseen domains. In subjective testing, human judges confirm that the procedure greatly improves generator performance when only a small amount of data is available in the domain.

Includes members:
  Member "multi domain dialog state tracking using recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "recent advances and challenges in task oriented dialog system")
  Member "stochastic language generation in dialogue using recurrent neural networks with convolutional sentence reranking":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "few shot natural language generation for task oriented dialog")
  Member "semantically conditioned lstm based natural language generation for spoken dialogue systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "recent advances and challenges in task oriented dialog system")
  Member "learning with augmented features for heterogeneous domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "domain adaptation through transferring both the source knowledge and target relatedness simultaneously")
  Member "theano new features and speed improvements":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mnn a universal and efficient inference engine")
  Member "frustratingly easy domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi source domain adaptation for text classification via distancenet bandits")
  Member "individual and domain adaptation in sentence planning for dialogue":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "towards end to end learning for efficient dialogue agent by modeling looking ahead ability")

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)