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: "response ranking with deep matching networks and external knowledge in information seeking conversation systems"
  Abstract: Intelligent personal assistant systems with either text-based or voice-based conversational interfaces are becoming increasingly popular around the world. Retrieval-based conversation models have the advantages of returning fluent and informative responses. Most existing studies in this area are on open domain ''chit-chat'' conversations or task / transaction oriented conversations. More research is needed for information-seeking conversations. There is also a lack of modeling external knowledge beyond the dialog utterances among current conversational models. In this paper, we propose a learning framework on the top of deep neural matching networks that leverages external knowledge for response ranking in information-seeking conversation systems. We incorporate external knowledge into deep neural models with pseudo-relevance feedback and QA correspondence knowledge distillation. Extensive experiments with three information-seeking conversation data sets including both open benchmarks and commercial data show that, our methods outperform various baseline methods including several deep text matching models and the state-of-the-art method on response selection in multi-turn conversations. We also perform analysis over different response types, model variations and ranking examples. Our models and research findings provide new insights on how to utilize external knowledge with deep neural models for response selection and have implications for the design of the next generation of information-seeking 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:
    - "a hybrid retrieval generation neural conversation model"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "a deep look into neural ranking models for information retrieval"):
  with members:
    - "learning groupwise scoring functions using deep neural networks"
    - "simple applications of bert for ad hoc document retrieval"
    - "semeval 2017 task 3 community question answering"
    - "user intent prediction in information seeking conversations"
    - "vitor learning to rank webpages based on visual features"
    - "wikipassageqa a benchmark collection for research on non factoid answer passage retrieval"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "cross domain regularization for neural ranking models using adversarial learning"

Connected through hyperedge HE-3 (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"
    - "a hybrid retrieval generation neural conversation model"

Connected through hyperedge HE-4 (induced by source paper "a hybrid retrieval generation neural conversation model"):
  with members:
    - "conversing by reading contentful neural conversation with on demand machine reading"
    - "a deep look into neural ranking models for information retrieval"
    - "anmm ranking short answer texts with attention based neural matching model"
    - "response generation by context aware prototype editing"
    - "transfer learning for context aware question matching in information seeking conversations in e commerce"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "neural approaches to conversational ai"
    - "generating informative and diverse conversational responses via adversarial information maximization"

Connected through hyperedge HE-5 (induced by source paper "attentive history selection for conversational question answering"):
  with members:
    - "bert with history answer embedding for conversational question answering"
    - "answer interaction in non factoid question answering systems"
    - "multi task deep neural networks for natural language understanding"
    - "user intent prediction in information seeking conversations"
    - "a deep look into neural ranking models for information retrieval"
    - "sdnet contextualized attention based deep network for conversational question answering"
    - "a qualitative comparison of coqa squad 2 0 and quac"
    - "analyzing and characterizing user intent in information seeking conversations"

Connected through hyperedge HE-6 (induced by source paper "dynamic search optimizing the game of information seeking"):
  with members:
    - "multi step retriever reader interaction for scalable open domain question answering"
    - "query tracking for e commerce conversational search a machine comprehension perspective"
    - "an algorithmic perspective on imitation learning"
    - "a reinforcement learning driven translation model for search oriented conversational systems"
    - "neural approaches to conversational ai"
    - "top k off policy correction for a reinforce recommender system"
    - "a click sequence model for web search"
    - "attentive memory networks efficient machine reading for conversational search"

Connected through hyperedge HE-7 (induced by source paper "learning to selectively transfer reinforced transfer learning for deep text matching"):
  with members:
    - "anmm ranking short answer texts with attention based neural matching model"
    - "reinforced co training"
    - "end task oriented textual entailment via deep explorations of inter sentence interactions"
    - "learning sampling policies for domain adaptation"
    - "reinforcement learning for relation classification from noisy data"
    - "alime assist an intelligent assistant for creating an innovative e commerce experience"
    - "learning how to active learn a deep reinforcement learning approach"
    - "deep reinforcement learning a brief survey"

Connected through hyperedge HE-8 (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"
    - "a hybrid retrieval generation neural conversation model"
    - "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"

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)