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: "asking clarifying questions in open domain information seeking conversations"
  Abstract: Users often fail to formulate their complex information needs in a single query. As a consequence, they may need to scan multiple result pages or reformulate their queries, which may be a frustrating experience. Alternatively, systems can improve user satisfaction by proactively asking questions of the users to clarify their information needs. Asking clarifying questions is especially important in conversational systems since they can only return a limited number of (often only one) result(s).   In this paper, we formulate the task of asking clarifying questions in open-domain information-seeking conversational systems. To this end, we propose an offline evaluation methodology for the task and collect a dataset, called Qulac, through crowdsourcing. Our dataset is built on top of the TREC Web Track 2009-2012 data and consists of over 10K question-answer pairs for 198 TREC topics with 762 facets. Our experiments on an oracle model demonstrate that asking only one good question leads to over 170% retrieval performance improvement in terms of P@1, which clearly demonstrates the potential impact of the task. We further propose a retrieval framework consisting of three components: question retrieval, question selection, and document retrieval. In particular, our question selection model takes into account the original query and previous question-answer interactions while selecting the next question. Our model significantly outperforms competitive baselines. To foster research in this area, we have made Qulac publicly available.

Connected through hyperedge HE-1 (induced by source paper "a tool for conducting user studies on mobile devices"):
  with members:
    - "understanding mobile search task relevance and user behaviour in context"
    - "target apps selection towards a unified search framework for mobile devices"
    - "mobile information retrieval"

Connected through hyperedge HE-2 (induced by source paper "unsupervised common question generation from multiple documents using reinforced contrastive coordinator"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "generating a common question from multiple documents using multi source encoder decoder models"
    - "passage re ranking with bert"
    - "generic intent representation in web search"
    - "a deep cascade model for multi document reading comprehension"
    - "towards coherent and cohesive long form text generation"
    - "multi style generative reading comprehension"
    - "deep communicating agents for abstractive summarization"

Connected through hyperedge HE-3 (induced by source paper "generating a common question from multiple documents using multi source encoder decoder models"):
  with members:
    - "passage re ranking with bert"
    - "a deep cascade model for multi document reading comprehension"
    - "multi style generative reading comprehension"
    - "deep communicating agents for abstractive summarization"
    - "input combination strategies for multi source transformer decoder"
    - "deep contextualized word representations"
    - "neural approaches to conversational ai"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-4 (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"
    - "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"
    - "learning to ask good questions ranking clarification questions using neural expected value of perfect information"

Connected through hyperedge HE-5 (induced by source paper "enriching conversation context in retrieval based chatbots"):
  with members:
    - "real time inference in multi sentence tasks with deep pretrained transformers"
    - "multi task deep neural networks for natural language understanding"
    - "knowledge incorporating esim models for response selection in retrieval based dialog systems"
    - "training millions of personalized dialogue agents"
    - "from eliza to xiaoice challenges and opportunities with social chatbots"
    - "neural approaches to conversational ai"
    - "alime assist an intelligent assistant for creating an innovative e commerce experience"
    - "enhance word representation for out of vocabulary on ubuntu dialogue corpus"

Connected through hyperedge HE-6 (induced by source paper "interactive classification by asking informative questions"):
  with members:
    - "user intent prediction in information seeking conversations"
    - "joint learning of interactive spoken content retrieval and trainable user simulator"
    - "adversarial domain adaptation for duplicate question detection"
    - "vision based navigation with language based assistance via imitation learning with indirect intervention"
    - "coqa a conversational question answering challenge"
    - "playing 20 question game with policy based reinforcement learning"
    - "learning to ask knowledge acquisition via 20 questions"
    - "quac question answering in context"

Connected through hyperedge HE-7 (induced by source paper "harnessing evolution of multi turn conversations for effective answer retrieval"):
  with members:
    - "conversational product search based on negative feedback"
    - "target apps selection towards a unified search framework for mobile devices"
    - "allennlp a deep semantic natural language processing platform"
    - "neural approaches to conversational ai"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "conversational recommender system"
    - "quac question answering in context"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "macaw an extensible conversational information seeking platform"):
  with members:
    - "towards deep conversational recommendations"
    - "coqa a conversational question answering challenge"
    - "joint modeling and optimization of search and recommendation"
    - "conversational recommender system"
    - "quac question answering in context"
    - "pyndri a python interface to the indri search engine"
    - "reading wikipedia to answer open domain questions"

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)