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 "judge the judges a large scale evaluation study of neural language models for online review generation"):
  Abstract: Recent advances in deep learning have resulted in a resurgence in the popularity of natural language generation (NLG). Many deep learning based models, including recurrent neural networks and generative adversarial networks, have been proposed and applied to generating various types of text. Despite the fast development of methods, how to better evaluate the quality of these natural language generators remains a significant challenge. We conduct an in-depth empirical study to evaluate the existing evaluation methods for natural language generation. We compare human-based evaluators with a variety of automated evaluation procedures, including discriminative evaluators that measure how well the generated text can be distinguished from human-written text, as well as text overlap metrics that measure how similar the generated text is to human-written references. We measure to what extent these different evaluators agree on the ranking of a dozen of state-of-the-art generators for online product reviews. We find that human evaluators do not correlate well with discriminative evaluators, leaving a bigger question of whether adversarial accuracy is the correct objective for natural language generation. In general, distinguishing machine-generated text is a challenging task even for human evaluators, and their decisions tend to correlate better with text overlap metrics. We also find that diversity is an intriguing metric that is indicative of the assessments of different evaluators.

Includes members:
  Member "unifying human and statistical evaluation for natural language generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards a human like open domain chatbot")
  Member "on accurate evaluation of gans for language generation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bert as a teacher contextual embeddings for sequence level reward")
  Member "neural text generation past present and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generalization in generation a closer look at exposure bias")
  Member "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cbag conditional biomedical abstract generation")
  Member "towards diverse text generation with inverse reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "self adversarial learning with comparative discrimination for text generation")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "texygen a benchmarking platform for text generation models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to generate multiple style transfer outputs for an input sentence")
  Member "evaluating text gans as language models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi object tracking via end to end tracklet searching and ranking")

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)