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: "avoiding echo responses in a retrieval based conversation system"
  Abstract: Retrieval-based conversation systems generally tend to highly rank responses that are semantically similar or even identical to the given conversation context. While the system’s goal is to find the most appropriate response, rather than the most semantically similar one, this tendency results in low-quality responses. We refer to this challenge as the echoing problem. To mitigate this problem, we utilize a hard negative mining approach at the training stage. The evaluation shows that the resulting model reduces echoing and achieves better results in terms of Average Precision and Recall@N metrics, compared to the models trained without the proposed approach.

Connected through hyperedge HE-1 (induced by source paper "a financial service chatbot based on deep bidirectional transformers"):
  with members:
    - "a comprehensive guide to bayesian convolutional neural network with variational inference"
    - "selectivenet a deep neural network with an integrated reject option"
    - "distilling task specific knowledge from bert into simple neural networks"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "an evaluation dataset for intent classification and out of scope prediction"
    - "roberta a robustly optimized bert pretraining approach"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "sampling based bayesian inference with gradient uncertainty"

Connected through hyperedge HE-2 (induced by source paper "knowledge based conversational search"):
  with members:
    - "message passing for complex question answering over knowledge graphs"
    - "query expansion techniques for information retrieval a survey"
    - "open data chatbot"
    - "towards a question answering system over the semantic web"
    - "earl joint entity and relation linking for question answering over knowledge graphs"
    - "modeling semantics with gated graph neural networks for knowledge base question answering"
    - "interpretation of natural language rules in conversational machine reading"
    - "explainable reasoning over knowledge graphs for recommendation"

Connected through hyperedge HE-3 (induced by source paper "why are sequence to sequence models so dull understanding the low diversity problem of chatbots"):
  with members:
    - "attention is all you need"
    - "parlai a dialog research software platform"
    - "generating high quality and informative conversation responses with sequence to sequence models"
    - "adversarial learning for neural dialogue generation"
    - "regularizing neural networks by penalizing confident output distributions"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "towards better decoding and language model integration in sequence to sequence models"
    - "a simple fast diverse decoding algorithm for neural generation"

Connected through hyperedge HE-4 (induced by source paper "neural response ranking for social conversation a data efficient approach"):
  with members:
    - "billion scale similarity search with gpus"
    - "a practical approach to dialogue response generation in closed domains"
    - "edina building an open domain socialbot with self dialogues"
    - "rubystar a non task oriented mixture model dialog system"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"
    - "an ensemble model with ranking for social dialogue"
    - "deep reinforcement learning for dialogue generation"
    - "conversational contextual cues the case of personalization and history for response ranking"

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)