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 "non autoregressive neural dialogue generation"):
  Abstract: Maximum Mutual information (MMI), which models the bidirectional dependency between responses ($y$) and contexts ($x$), i.e., the forward probability $\log p(y|x)$ and the backward probability $\log p(x|y)$, has been widely used as the objective in the \sts model to address the dull-response issue in open-domain dialog generation. Unfortunately, under the framework of the \sts model, direct decoding from $\log p(y|x) + \log p(x|y)$ is infeasible since the second part (i.e., $p(x|y)$) requires the completion of target generation before it can be computed, and the search space for $y$ is enormous. Empirically, an N-best list is first generated given $p(y|x)$, and $p(x|y)$ is then used to rerank the N-best list, which inevitably results in non-globally-optimal solutions. In this paper, we propose to use non-autoregressive (non-AR) generation model to address this non-global optimality issue. Since target tokens are generated independently in non-AR generation, $p(x|y)$ for each target word can be computed as soon as it's generated, and does not have to wait for the completion of the whole sequence. This naturally resolves the non-global optimal issue in decoding. Experimental results demonstrate that the proposed non-AR strategy produces more diverse, coherent, and appropriate responses, yielding substantive gains in BLEU scores and in human evaluations.

Includes members:
  Member "avgout a simple output probability measure to eliminate dull responses":
    also connected through other hyperedges:
      (none)
  Member "towards a human like open domain chatbot":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "xpersona evaluating multilingual personalized chatbot")
  Member "latent variable non autoregressive neural machine translation with deterministic inference using a delta posterior":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "parallel machine translation with disentangled context transformer")
  Member "comparison of diverse decoding methods from conditional language models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "relevance promoting language model for short text conversation")
  Member "fast structured decoding for sequence models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "parallel machine translation with disentangled context transformer")
  Member "complexity weighted loss and diverse reranking for sentence simplification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "comparison of diverse decoding methods from conditional language models")
  Member "pnat non autoregressive transformer by position learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lava nat a non autoregressive translation model with look around decoding and vocabulary attention")
  Member "dialogpt large scale generative pre training for conversational response generation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "xpersona evaluating multilingual personalized chatbot")

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)