human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "ruber an unsupervised method for automatic evaluation of open domain dialog systems"
  Abstract: Open-domain human-computer conversation has been attracting increasing attention over the past few years. However, there does not exist a standard automatic evaluation metric for open-domain dialog systems; researchers usually resort to human annotation for model evaluation, which is time- and labor-intensive. In this paper, we propose RUBER, a Referenced metric and Unreferenced metric Blended Evaluation Routine, which evaluates a reply by taking into consideration both a groundtruth reply and a query (previous user-issued utterance). Our metric is learnable, but its training does not require labels of human satisfaction. Hence, RUBER is flexible and extensible to different datasets and languages. Experiments on both retrieval and generative dialog systems show that RUBER has a high correlation with human annotation.

Connected through hyperedge HE-1 (induced by source paper "towards a human like open domain chatbot"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "better automatic evaluation of open domain dialogue systems with contextualized embeddings"
    - "the curious case of neural text degeneration"
    - "unifying human and statistical evaluation for natural language generation"
    - "human and automatic detection of generated text"
    - "ctrl a conditional transformer language model for controllable generation"
    - "jointly optimizing diversity and relevance in neural response generation"
    - "the evolved transformer"

Connected through hyperedge HE-2 (induced by source paper "improving neural conversational models with entropy based data filtering"):
  with members:
    - "deep learning based chatbot models"
    - "jointly optimizing diversity and relevance in neural response generation"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "importance of search and evaluation strategies in neural dialogue modeling"
    - "neural response ranking for social conversation a data efficient approach"
    - "automatic evaluation of neural personality based chatbots"
    - "better conversations by modeling filtering and optimizing for coherence and diversity"
    - "towards explainable and controllable open domain dialogue generation with dialogue acts"

Connected through hyperedge HE-3 (induced by source paper "low resource response generation with template prior"):
  with members:
    - "meta learning for low resource natural language generation in task oriented dialogue systems"
    - "text generation with exemplar based adaptive decoding"
    - "dual latent variable model for low resource natural language generation in dialogue systems"
    - "universal neural machine translation for extremely low resource languages"
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "learning neural templates for text generation"
    - "conversational ai the science behind the alexa prize"
    - "generating sentences by editing prototypes"

Connected through hyperedge HE-4 (induced by source paper "adversarial language games for advanced natural language intelligence"):
  with members:
    - "emergent tool use from multi agent autocurricula"
    - "better automatic evaluation of open domain dialogue systems with contextualized embeddings"
    - "on evaluation of adversarial perturbations for sequence to sequence models"
    - "universal adversarial triggers for attacking and analyzing nlp"
    - "egg a toolkit for research on emergence of language in games"
    - "robust neural machine translation with doubly adversarial inputs"
    - "adversarial attacks on deep learning models in natural language processing a survey"
    - "robust machine comprehension models via adversarial training"

Connected through hyperedge HE-5 (induced by source paper "towards coherent and engaging spoken dialog response generation using automatic conversation evaluators"):
  with members:
    - "learning to write with cooperative discriminators"
    - "topic based evaluation for conversational bots"
    - "an efficient framework for learning sentence representations"
    - "contextual topic modeling for dialog systems"
    - "coherence models for dialogue"
    - "conversational ai the science behind the alexa prize"
    - "on evaluating and comparing open domain dialog systems"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "araml a stable adversarial training framework for text generation"):
  with members:
    - "incorporating discriminator in sentence generation a gibbs sampling method"
    - "on accurate evaluation of gans for language generation"
    - "towards diverse text generation with inverse reinforcement learning"
    - "texygen a benchmarking platform for text generation models"
    - "cgmh constrained sentence generation by metropolis hastings sampling"
    - "language gans falling short"
    - "the price of debiasing automatic metrics in natural language evaluation"
    - "emotional chatting machine emotional conversation generation with internal and external memory"

Connected through hyperedge HE-7 (induced by source paper "investigating evaluation of open domain dialogue systems with human generated multiple references"):
  with members:
    - "re evaluating adem a deeper look at scoring dialogue responses"
    - "unifying human and statistical evaluation for natural language generation"
    - "jointly optimizing diversity and relevance in neural response generation"
    - "survey on evaluation methods for dialogue systems"
    - "importance of search and evaluation strategies in neural dialogue modeling"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "importance of a search strategy in neural dialogue modelling"
    - "texygen a benchmarking platform for text generation models"

Connected through hyperedge HE-8 (induced by source paper "product aware answer generation in e commerce question answering"):
  with members:
    - "joint training of candidate extraction and answer selection for reading comprehension"
    - "learning to remember translation history with a continuous cache"
    - "an abstractive approach to question answering"
    - "query based abstractive summarization using neural networks"
    - "modelling domain relationships for transfer learning on retrieval based question answering systems in e commerce"
    - "get to the point summarization with pointer generator networks"
    - "improved training of wasserstein gans"
    - "s net from answer extraction to answer generation for machine reading comprehension"

Question: Please tell me which class the center node 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)