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 "towards best experiment design for evaluating dialogue system output"):
  Abstract: To overcome the limitations of automated metrics (e.g. BLEU, METEOR) for evaluating dialogue systems, researchers typically use human judgments to provide convergent evidence. While it has been demonstrated that human judgments can suffer from the inconsistency of ratings, extant research has also found that the design of the evaluation task affects the consistency and quality of human judgments. We conduct a between-subjects study to understand the impact of four experiment conditions on human ratings of dialogue system output. In addition to discrete and continuous scale ratings, we also experiment with a novel application of Best-Worst scaling to dialogue evaluation. Through our systematic study with 40 crowdsourced workers in each task, we find that using continuous scales achieves more consistent ratings than Likert scale or ranking-based experiment design. Additionally, we find that factors such as time taken to complete the task and no prior experience of participating in similar studies of rating dialogue system output positively impact consistency and agreement amongst raters

Includes members:
  Member "transfertransfo a transfer learning approach for neural network based conversational agents":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "recent advances and challenges in task oriented dialog system")
  Member "evaluating coherence in dialogue systems using entailment":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "studying the effects of cognitive biases in evaluation of conversational agents")
  Member "a survey of natural language generation techniques with a focus on dialogue systems past present and future directions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "introducing aspects of creativity in automatic poetry generation")
  Member "rankme reliable human ratings for natural language generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "studying the effects of cognitive biases in evaluation of conversational agents")
  Member "personalizing dialogue agents i have a dog do you have pets too":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "attention over parameters for dialogue systems")
  Member "augmenting neural response generation with context aware topical attention":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "low resource knowledge grounded dialogue generation")
  Member "survey of the state of the art in natural language generation core tasks applications and evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "studying the effects of cognitive biases in evaluation of conversational agents")
  Member "hierarchical recurrent attention network for response generation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a survey of natural language generation techniques with a focus on dialogue systems past present and future directions")

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)