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 "tree structured semantic encoder with knowledge sharing for domain adaptation in natural language generation"):
  Abstract: Domain adaptation in natural language generation (NLG) remains challenging because of the high complexity of input semantics across domains and limited data of a target domain. This is particularly the case for dialogue systems, where we want to be able to seamlessly include new domains into the conversation. Therefore, it is crucial for generation models to share knowledge across domains for the effective adaptation from one domain to another. In this study, we exploit a tree-structured semantic encoder to capture the internal structure of complex semantic representations required for multi-domain dialogues in order to facilitate knowledge sharing across domains. In addition, a layer-wise attention mechanism between the tree encoder and the decoder is adopted to further improve the model's capability. The automatic evaluation results show that our model outperforms previous methods in terms of the BLEU score and the slot error rate, in particular when the adaptation data is limited. In subjective evaluation, human judges tend to prefer the sentences generated by our model, rating them more highly on informativeness and naturalness than other systems.

Includes members:
  Member "variational cross domain natural language generation for spoken dialogue systems":
    also connected through other hyperedges:
      (none)
  Member "controlling personality based stylistic variation with neural natural language generators":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "evaluating the state of the art of end to end natural language generation the e2e nlg challenge")
  Member "natural language generation by hierarchical decoding with linguistic patterns":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "evaluating the state of the art of end to end natural language generation the e2e nlg challenge")
  Member "adversarial domain adaptation for variational neural language generation in dialogue systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "how to build user simulators to train rl based dialog systems")
  Member "natural language generation for spoken dialogue system using rnn encoder decoder networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "few shot natural language generation for task oriented dialog")
  Member "the e2e dataset new challenges for end to end generation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "few shot natural language generation for task oriented dialog")
  Member "multi domain neural network language generation for spoken dialogue systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "few shot natural language generation for task oriented dialog")
  Member "improved semantic representations from tree structured long short term memory networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unbiased scene graph generation from biased training")

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)