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 "dialogue transformers"):
  Abstract: We introduce a dialogue policy based on a transformer architecture, where the self-attention mechanism operates over the sequence of dialogue turns. Recent work has used hierarchical recurrent neural networks to encode multiple utterances in a dialogue context, but we argue that a pure self-attention mechanism is more suitable. By default, an RNN assumes that every item in a sequence is relevant for producing an encoding of the full sequence, but a single conversation can consist of multiple overlapping discourse segments as speakers interleave multiple topics. A transformer picks which turns to include in its encoding of the current dialogue state, and is naturally suited to selectively ignoring or attending to dialogue history. We compare the performance of the Transformer Embedding Dialogue (TED) policy to an LSTM and to the REDP, which was specifically designed to overcome this limitation of RNNs. We show that the TED policy's behaviour compares favourably, both in terms of accuracy and speed.

Includes members:
  Member "multiwoz 2 1 multi domain dialogue state corrections and state tracking baselines":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ma dst multi attention based scalable dialog state tracking")
  Member "training neural response selection for task oriented dialogue systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient intent detection with dual sentence encoders")
  Member "pretraining methods for dialog context representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a knowledge enhanced pretraining model for commonsense story generation")
  Member "sparse sequence to sequence models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "power bert accelerating bert inference for classification tasks")
  Member "transformer xl attentive language models beyond a fixed length context":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on contextual embeddings")
  Member "modeling intent dialog policies and response adaptation for goal oriented interactions":
    also connected through other hyperedges:
      (none)
  Member "few shot generalization across dialogue tasks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "domain adaptive dialog generation via meta learning")
  Member "glue a multi task benchmark and analysis platform for natural language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)