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: "recosa detecting the relevant contexts with self attention for multi turn dialogue generation"
  Abstract: In multi-turn dialogue generation, response is usually related with only a few contexts. Therefore, an ideal model should be able to detect these relevant contexts and produce a suitable response accordingly. However, the widely used hierarchical recurrent encoderdecoder models just treat all the contexts indiscriminately, which may hurt the following response generation process. Some researchers try to use the cosine similarity or the traditional attention mechanism to find the relevant contexts, but they suffer from either insufficient relevance assumption or position bias problem. In this paper, we propose a new model, named ReCoSa, to tackle this problem. Firstly, a word level LSTM encoder is conducted to obtain the initial representation of each context. Then, the self-attention mechanism is utilized to update both the context and masked response representation. Finally, the attention weights between each context and response representations are computed and used in the further decoding process. Experimental results on both Chinese customer services dataset and English Ubuntu dialogue dataset show that ReCoSa significantly outperforms baseline models, in terms of both metric-based and human evaluations. Further analysis on attention shows that the detected relevant contexts by ReCoSa are highly coherent with human's understanding, validating the correctness and interpretability of ReCoSa.

Connected through hyperedge HE-1 (induced by source paper "learning from easy to complex adaptive multi curricula learning for neural dialogue generation"):
  with members:
    - "improving neural conversational models with entropy based data filtering"
    - "what makes a good conversation how controllable attributes affect human judgments"
    - "competence based curriculum learning for neural machine translation"
    - "simple and effective curriculum pointer generator networks for reading comprehension over long narratives"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "better conversations by modeling filtering and optimizing for coherence and diversity"
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "personalizing dialogue agents i have a dog do you have pets too"

Connected through hyperedge HE-2 (induced by source paper "when to talk chatbot controls the timing of talking during multi turn open domain dialogue generation"):
  with members:
    - "bertscore evaluating text generation with bert"
    - "fast graph representation learning with pytorch geometric"
    - "modeling semantic relationship in multi turn conversations with hierarchical latent variables"
    - "graph neural networks a review of methods and applications"
    - "graph convolution over pruned dependency trees improves relation extraction"
    - "deep learning on graphs a survey"
    - "dailydialog a manually labelled multi turn dialogue dataset"
    - "a survey on dialogue systems recent advances and new frontiers"

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)