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: "evaluating coherence in dialogue systems using entailment"
  Abstract: Evaluating open-domain dialogue systems is difficult due to the diversity of possible correct answers. Automatic metrics such as BLEU correlate weakly with human annotations, resulting in a significant bias across different models and datasets. Some researchers resort to human judgment experimentation for assessing response quality, which is expensive, time consuming, and not scalable. Moreover, judges tend to evaluate a small number of dialogues, meaning that minor differences in evaluation configuration may lead to dissimilar results. In this paper, we present interpretable metrics for evaluating topic coherence by making use of distributed sentence representations. Furthermore, we introduce calculable approximations of human judgment based on conversational coherence by adopting state-of-the-art entailment techniques. Results show that our metrics can be used as a surrogate for human judgment, making it easy to evaluate dialogue systems on large-scale datasets and allowing an unbiased estimate for the quality of the responses.

Connected through hyperedge HE-1 (induced by source paper "studying the effects of cognitive biases in evaluation of conversational agents"):
  with members:
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "rankme reliable human ratings for natural language generation"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "augmenting neural response generation with context aware topical attention"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "a knowledge grounded neural conversation model"
    - "attention is all you need"

Connected through hyperedge HE-2 (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"
    - "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"
    - "team qcri mit at semeval 2019 task 4 propaganda analysis meets hyperpartisan news detection"

Connected through hyperedge HE-3 (induced by source paper "generating persona consistent dialogues by exploiting natural language inference"):
  with members:
    - "mass masked sequence to sequence pre training for language generation"
    - "exploiting persona information for diverse generation of conversational responses"
    - "a study of reinforcement learning for neural machine translation"
    - "dialogue natural language inference"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "deep reinforcement learning for chinese zero pronoun resolution"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "semantic sentence matching with densely connected recurrent and co attentive information"

Connected through hyperedge HE-4 (induced by source paper "towards best experiment design for evaluating dialogue system output"):
  with members:
    - "transfertransfo a transfer learning approach for neural network based conversational agents"
    - "a survey of natural language generation techniques with a focus on dialogue systems past present and future directions"
    - "rankme reliable human ratings for natural language generation"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "augmenting neural response generation with context aware topical attention"
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "hierarchical recurrent attention network for response generation"
    - "towards an automatic turing test learning to evaluate dialogue responses"

Connected through hyperedge HE-5 (induced by source paper "a neural model for dialogue coherence assessment"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "reward learning for efficient reinforcement learning in extractive document summarisation"
    - "multi task learning for coherence modeling"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "better conversations by modeling filtering and optimizing for coherence and diversity"
    - "why comparing single performance scores does not allow to draw conclusions about machine learning approaches"
    - "measuring semantic coherence of a conversation"
    - "coherence models for dialogue"

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)