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 "mala cross domain dialogue generation with action learning"):
  Abstract: Response generation for task-oriented dialogues involves two basic components: dialogue planning and surface realization. These two components, however, have a discrepancy in their objectives, i.e., task completion and language quality. To deal with such discrepancy, conditioned response generation has been introduced where the generation process is factorized into action decision and language generation via explicit action representations. To obtain action representations, recent studies learn latent actions in an unsupervised manner based on the utterance lexical similarity. Such an action learning approach is prone to diversities of language surfaces, which may impinge task completion and language quality. To address this issue, we propose multi-stage adaptive latent action learning (MALA) that learns semantic latent actions by distinguishing the effects of utterances on dialogue progress. We model the utterance effect using the transition of dialogue states caused by the utterance and develop a semantic similarity measurement that estimates whether utterances have similar effects. For learning semantic actions on domains without dialogue states, MsALA extends the semantic similarity measurement across domains progressively, i.e., from aligning shared actions to learning domain-specific actions. Experiments using multi-domain datasets, SMD and MultiWOZ, show that our proposed model achieves consistent improvements over the baselines models in terms of both task completion and language quality.

Includes members:
  Member "domain adaptive dialog generation via meta learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "recent advances and challenges in task oriented dialog system")
  Member "carl aggregated search with context aware module embedding learning":
    also connected through other hyperedges:
      (none)
  Member "rethinking action spaces for reinforcement learning in end to end dialog agents with latent variable models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "convlab 2 an open source toolkit for building evaluating and diagnosing dialogue systems")
  Member "semantically conditioned dialog response generation via hierarchical disentangled self attention":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "few shot natural language generation for task oriented dialog")
  Member "transferable multi domain state generator for task oriented dialogue systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "recent advances and challenges in task oriented dialog system")
  Member "zero shot dialog generation with cross domain latent actions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning to memorize in neural task oriented dialogue systems")
  Member "unsupervised text style transfer using language models as discriminators":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "blank language models")
  Member "unsupervised discrete sentence representation learning for interpretable neural dialog generation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "toward interpretability of dual encoder models for dialogue response suggestions")

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)