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: "ethical challenges in data driven dialogue systems"
  Abstract: The use of dialogue systems as a medium for human-machine interaction is an increasingly prevalent paradigm. A growing number of dialogue systems use conversation strategies that are learned from large datasets. There are well documented instances where interactions with these system have resulted in biased or even offensive conversations due to the data-driven training process. Here, we highlight potential ethical issues that arise in dialogue systems research, including: implicit biases in data-driven systems, the rise of adversarial examples, potential sources of privacy violations, safety concerns, special considerations for reinforcement learning systems, and reproducibility concerns. We also suggest areas stemming from these issues that deserve further investigation. Through this initial survey, we hope to spur research leading to robust, safe, and ethically sound dialogue systems.

Connected through hyperedge HE-1 (induced by source paper "hierarchical reinforcement learning for open domain dialog"):
  with members:
    - "do neural dialog systems use the conversation history effectively an empirical study"
    - "approximating interactive human evaluation with self play for open domain dialog systems"
    - "happybot generating empathetic dialogue responses by improving user experience look ahead"
    - "evaluating coherence in dialogue systems using entailment"
    - "universal adversarial triggers for attacking and analyzing nlp"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "deep reinforcement learning for modeling chit chat dialog with discrete attributes"
    - "way off policy batch deep reinforcement learning of implicit human preferences in dialog"

Connected through hyperedge HE-2 (induced by source paper "queens are powerful too mitigating gender bias in dialogue generation"):
  with members:
    - "don t take the easy way out ensemble based methods for avoiding known dataset biases"
    - "does gender matter towards fairness in dialogue systems"
    - "it s all in the name mitigating gender bias with name based counterfactual data substitution"
    - "unlearn dataset bias in natural language inference by fitting the residual"
    - "acute eval improved dialogue evaluation with optimized questions and multi turn comparisons"
    - "learning to speak and act in a fantasy text adventure game"
    - "gender bias in contextualized word embeddings"
    - "what makes a good conversation how controllable attributes affect human judgments"

Connected through hyperedge HE-3 (induced by source paper "does gender matter towards fairness in dialogue systems"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "a survey on bias and fairness in machine learning"
    - "compositional fairness constraints for graph embeddings"
    - "scalable fair clustering"
    - "identifying and reducing gender bias in word level language models"
    - "build it break it fix it for dialogue safety robustness from adversarial human attack"
    - "say what i want towards the dark side of neural dialogue models"
    - "assessing gender bias in machine translation a case study with google translate"

Connected through hyperedge HE-4 (induced by source paper "build it break it fix it for dialogue safety robustness from adversarial human attack"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "semeval 2019 task 6 identifying and categorizing offensive language in social media offenseval"
    - "all you need is love evading hate speech detection"
    - "large scale gan training for high fidelity natural image synthesis"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "automated hate speech detection and the problem of offensive language"
    - "deceiving google s perspective api built for detecting toxic comments"
    - "anyone can become a troll causes of trolling behavior in online discussions"

Connected through hyperedge HE-5 (induced by source paper "the woman worked as a babysitter on biases in language generation"):
  with members:
    - "examining gender bias in languages with grammatical gender"
    - "assessing gender bias in machine translation a case study with google translate"
    - "examining gender and race bias in two hundred sentiment analysis systems"
    - "gender bias in coreference resolution"
    - "gender bias in neural natural language processing"
    - "gender bias in coreference resolution evaluation and debiasing methods"
    - "exploring the limits of language modeling"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"

Connected through hyperedge HE-6 (induced by source paper "a crowd based evaluation of abuse response strategies in conversational agents"):
  with members:
    - "rankme reliable human ratings for natural language generation"
    - "an ensemble model with ranking for social dialogue"
    - "a neural conversational model"

Connected through hyperedge HE-7 (induced by source paper "the rllchatbot a solution to the convai challenge"):
  with members:
    - "aiming to know you better perhaps makes me a more engaging dialogue partner"
    - "learning to ask neural question generation for reading comprehension"
    - "attention is all you need"
    - "a deep reinforcement learning chatbot"
    - "deep reinforcement learning for dialogue generation"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "squad 100 000 questions for machine comprehension of text"
    - "on the evaluation of dialogue systems with next utterance classification"

Connected through hyperedge HE-8 (induced by source paper "fighting offensive language on social media with unsupervised text style transfer"):
  with members:
    - "toward controlled generation of text"
    - "automated hate speech detection and the problem of offensive language"
    - "unsupervised machine translation using monolingual corpora only"
    - "controlling linguistic style aspects in neural language generation"
    - "unsupervised neural machine translation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "style transfer in text exploration and evaluation"
    - "neural machine translation by jointly learning to align and translate"

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)