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 "conditional generation and snapshot learning in neural dialogue systems"):
  Abstract: Recently a variety of LSTM-based conditional language models (LM) have been applied across a range of language generation tasks. In this work we study various model architectures and different ways to represent and aggregate the source information in an end-to-end neural dialogue system framework. A method called snapshot learning is also proposed to facilitate learning from supervised sequential signals by applying a companion cross-entropy objective function to the conditioning vector. The experimental and analytical results demonstrate firstly that competition occurs between the conditioning vector and the LM, and the differing architectures provide different trade-offs between the two. Secondly, the discriminative power and transparency of the conditioning vector is key to providing both model interpretability and better performance. Thirdly, snapshot learning leads to consistent performance improvements independent of which architecture is used.

Includes members:
  Member "end to end memory networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning video object segmentation from unlabeled videos")
  Member "what to talk about and how selective generation using lstms with coarse to fine alignment":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "variational template machine for data to text generation")
  Member "evaluating prerequisite qualities for learning end to end dialog systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "self attentional models application in task oriented dialogue generation systems")
  Member "the ubuntu dialogue corpus a large dataset for research in unstructured multi turn dialogue systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "toward interpretability of dual encoder models for dialogue response suggestions")
  Member "show attend and tell neural image caption generation with visual attention":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "the goldilocks principle reading children s books with explicit memory representations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on contextual embeddings")
  Member "neural responding machine for short text conversation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an empirical investigation of pre trained transformer language models for open domain dialogue generation")
  Member "learning from real users rating dialogue success with neural networks for reinforcement learning in spoken dialogue systems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "improving interaction quality estimation with bilstms and the impact on dialogue policy learning")

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)