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: "joint modeling and optimization of search and recommendation"
  Abstract: Despite the somewhat different techniques used in developing search engines and recommender systems, they both follow the same goal: helping people to get the information they need at the right time. Due to this common goal, search and recommendation models can potentially benefit from each other. The recent advances in neural network technologies make them effective and easily extendable for various tasks, including retrieval and recommendation. This raises the possibility of jointly modeling and optimizing search ranking and recommendation algorithms, with potential benefits to both. In this paper, we present theoretical and practical reasons to motivate joint modeling of search and recommendation as a research direction. We propose a general framework that simultaneously learns a retrieval model and a recommendation model by optimizing a joint loss function. Our preliminary results on a dataset of product data indicate that the proposed joint modeling substantially outperforms the retrieval and recommendation models trained independently. We list a number of future directions for this line of research that can potentially lead to development of state-of-the-art search and recommendation models.

Connected through hyperedge HE-1 (induced by source paper "learning to ask question based sequential bayesian product search"):
  with members:
    - "technology assisted reviews finding the last few relevant documents by asking yes no questions to reviewers"
    - "playing 20 question game with policy based reinforcement learning"
    - "attentive long short term preference modeling for personalized product search"
    - "learning to ask knowledge acquisition via 20 questions"
    - "conversational recommender system"
    - "word entity duet representations for document ranking"
    - "learning latent vector spaces for product search"
    - "autonomy and reliability of continuous active learning for technology assisted review"

Connected through hyperedge HE-2 (induced by source paper "macaw an extensible conversational information seeking platform"):
  with members:
    - "asking clarifying questions in open domain information seeking conversations"
    - "towards deep conversational recommendations"
    - "coqa a conversational question answering challenge"
    - "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.IR(Information Retrieval)