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: "a hybrid retrieval generation neural conversation model"
  Abstract: Intelligent personal assistant systems that are able to have multi-turn conversations with human users are becoming increasingly popular. Most previous research has been focused on using either retrieval-based or generation-based methods to develop such systems. Retrieval-based methods have the advantage of returning fluent and informative responses with great diversity. However, the performance of the methods is limited by the size of the response repository. On the other hand, generation-based methods can produce highly coherent responses on any topics. But the generated responses are often generic and not informative due to the lack of grounding knowledge. In this paper, we propose a hybrid neural conversation model that combines the merits of both response retrieval and generation methods. Experimental results on Twitter and Foursquare data show that the proposed model outperforms both retrieval-based methods and generation-based methods (including a recently proposed knowledge-grounded neural conversation model) under both automatic evaluation metrics and human evaluation. We hope that the findings in this study provide new insights on how to integrate text retrieval and text generation models for building conversation systems.

Connected through hyperedge HE-1 (induced by source paper "iart intent aware response ranking with transformers in information seeking conversation systems"):
  with members:
    - "attentive history selection for conversational question answering"
    - "user intent prediction in information seeking conversations"
    - "a deep look into neural ranking models for information retrieval"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "analyzing and characterizing user intent in information seeking conversations"
    - "neural approaches to conversational ai"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "alime assist an intelligent assistant for creating an innovative e commerce experience"

Connected through hyperedge HE-2 (induced by source paper "curriculum learning strategies for ir an empirical study on conversation response ranking"):
  with members:
    - "bert with history answer embedding for conversational question answering"
    - "passage re ranking with bert"
    - "introducing mantis a novel multi domain information seeking dialogues dataset"
    - "faq retrieval using query question similarity and bert based query answer relevance"
    - "on the power of curriculum learning in training deep networks"
    - "image difficulty curriculum for generative adversarial networks cugan"
    - "simple applications of bert for ad hoc document retrieval"
    - "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models"

Connected through hyperedge HE-3 (induced by source paper "conversing by reading contentful neural conversation with on demand machine reading"):
  with members:
    - "dialog system technology challenge 7"
    - "jointly optimizing diversity and relevance in neural response generation"
    - "challenges in building intelligent open domain dialog systems"
    - "consistent dialogue generation with self supervised feature learning"
    - "coqa a conversational question answering challenge"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "personalizing dialogue agents i have a dog do you have pets too"
    - "hierarchical neural story generation"

Connected through hyperedge HE-4 (induced by source paper "atom commit message generation based on abstract syntax tree and hybrid ranking"):
  with members:
    - "generating commit messages from git diffs"
    - "devign effective vulnerability identification by learning comprehensive program semantics via graph neural networks"
    - "code2vec learning distributed representations of code"
    - "a general path based representation for predicting program properties"
    - "improving automatic source code summarization via deep reinforcement learning"
    - "code2seq generating sequences from structured representations of code"
    - "attention is all you need"
    - "automatically generating commit messages from diffs using neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "introducing mantis a novel multi domain information seeking dialogues dataset"):
  with members:
    - "simple applications of bert for ad hoc document retrieval"
    - "asking clarifying questions in open domain information seeking conversations"
    - "user intent prediction in information seeking conversations"
    - "interpretation of natural language rules in conversational machine reading"
    - "analyzing and characterizing user intent in information seeking conversations"
    - "integrating planning for task completion dialogue policy learning"
    - "subgoal discovery for hierarchical dialogue policy learning"
    - "learning to ask good questions ranking clarification questions using neural expected value of perfect information"

Connected through hyperedge HE-6 (induced by source paper "building a production model for retrieval based chatbots"):
  with members:
    - "diversifying reply suggestions using a matching conditional variational autoencoder"
    - "neural response ranking for social conversation a data efficient approach"
    - "an efficient framework for learning sentence representations"
    - "modeling multi turn conversation with deep utterance aggregation"
    - "simple recurrent units for highly parallelizable recurrence"
    - "attention is all you need"
    - "a practical approach to dialogue response generation in closed domains"
    - "a structured self attentive sentence embedding"

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.IR(Information Retrieval)