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: "analyzing and characterizing user intent in information seeking conversations"
  Abstract: Understanding and characterizing how people interact in information-seeking conversations is crucial in developing conversational search systems. In this paper, we introduce a new dataset designed for this purpose and use it to analyze information-seeking conversations by user intent distribution, co-occurrence, and flow patterns. The MSDialog dataset is a labeled dialog dataset of question answering (QA) interactions between information seekers and providers from an online forum on Microsoft products. The dataset contains more than 2,000 multi-turn QA dialogs with 10,000 utterances that are annotated with user intent on the utterance level. Annotations were done using crowdsourcing. With MSDialog, we find some highly recurring patterns in user intent during an information-seeking process. They could be useful for designing conversational search systems. We will make our dataset freely available to encourage exploration of information-seeking conversation models.

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"
    - "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 "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 "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"
    - "know what you don t know unanswerable questions for squad"

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"
    - "a hybrid retrieval generation neural conversation model"
    - "user intent prediction in information seeking conversations"
    - "interpretation of natural language rules in conversational machine reading"
    - "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 "asking clarifying questions in open domain information seeking conversations"):
  with members:
    - "answer based adversarial training for generating clarification questions"
    - "investigating the successes and failures of bert for passage re ranking"
    - "a deep look into neural ranking models for information retrieval"
    - "target apps selection towards a unified search framework for mobile devices"
    - "learning to ask questions in open domain conversational systems with typed decoders"
    - "transfer learning for context aware question matching in information seeking conversations in e commerce"
    - "learning to ask good questions ranking clarification questions using neural expected value of perfect information"
    - "coqa a conversational question answering challenge"

Connected through hyperedge HE-7 (induced by source paper "bert with history answer embedding for conversational question answering"):
  with members:
    - "user intent prediction in information seeking conversations"
    - "sdnet contextualized attention based deep network for conversational question answering"
    - "coqa a conversational question answering challenge"
    - "neural approaches to conversational ai"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "quac question answering in context"
    - "response ranking with deep matching networks and external knowledge in information seeking conversation systems"
    - "flowqa grasping flow in history for conversational machine comprehension"

Connected through hyperedge HE-8 (induced by source paper "conversational product search based on negative feedback"):
  with members:
    - "a study of context dependencies in multi page product search"
    - "conversational recommender system"
    - "iterative relevance feedback for answer passage retrieval with passage level semantic match"
    - "attentive memory networks efficient machine reading for conversational search"
    - "on application of learning to rank for e commerce search"
    - "learning latent vector spaces for product search"
    - "inferring networks of substitutable and complementary products"
    - "when relevance is not enough promoting visual attractiveness for fashion e commerce"

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)