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 "rankme reliable human ratings for natural language generation"):
  Abstract: Human evaluation for natural language generation (NLG) often suffers from inconsistent user ratings. While previous research tends to attribute this problem to individual user preferences, we show that the quality of human judgements can also be improved by experimental design. We present a novel rank-based magnitude estimation method (RankME), which combines the use of continuous scales and relative assessments. We show that RankME significantly improves the reliability and consistency of human ratings compared to traditional evaluation methods. In addition, we show that it is possible to evaluate NLG systems according to multiple, distinct criteria, which is important for error analysis. Finally, we demonstrate that RankME, in combination with Bayesian estimation of system quality, is a cost-effective alternative for ranking multiple NLG systems.

Includes members:
  Member "survey of the state of the art in natural language generation core tasks applications and evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "studying the effects of cognitive biases in evaluation of conversational agents")
  Member "referenceless quality estimation for natural language generation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "evaluating the state of the art of end to end natural language generation the e2e nlg challenge")
  Member "towards an automatic turing test learning to evaluate dialogue responses":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards a human like open domain chatbot")
  Member "challenges in data to document generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "variational template machine for data to text generation")
  Member "the e2e dataset new challenges for end to end generation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "few shot natural language generation for task oriented dialog")
  Member "natural language generation in dialogue using lexicalized and delexicalized data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "evaluating the state of the art of end to end natural language generation the e2e nlg challenge")
  Member "sequence to sequence generation for spoken dialogue via deep syntax trees and strings":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "stochastic natural language generation using dependency information")
  Member "natural language generation enhances human decision making with uncertain information":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "explanation in human ai systems a literature meta review synopsis of key ideas and publications and bibliography for explainable ai")

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)