human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "estimation action reflection towards deep interaction between conversational and recommender systems"):
  Abstract: Recommender systems are embracing conversational technologies to obtain user preferences dynamically, and to overcome inherent limitations of their static models. A successful Conversational Recommender System (CRS) requires proper handling of interactions between conversation and recommendation. We argue that three fundamental problems need to be solved: 1) what questions to ask regarding item attributes, 2) when to recommend items, and 3) how to adapt to the users' online feedback. To the best of our knowledge, there lacks a unified framework that addresses these problems. #R##N#In this work, we fill this missing interaction framework gap by proposing a new CRS framework named Estimation-Action-Reflection, or EAR, which consists of three stages to better converse with users. (1) Estimation, which builds predictive models to estimate user preference on both items and item attributes; (2) Action, which learns a dialogue policy to determine whether to ask attributes or recommend items, based on Estimation stage and conversation history; and (3) Reflection, which updates the recommender model when a user rejects the recommendations made by the Action stage. We present two conversation scenarios on binary and enumerated questions, and conduct extensive experiments on two datasets from Yelp and LastFM, for each scenario, respectively. Our experiments demonstrate significant improvements over the state-of-the-art method CRM [32], corresponding to fewer conversation turns and a higher level of recommendation hits.

Includes members:
  Member "deep conversational recommender in travel":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards knowledge based recommender dialog system")
  Member "mmalfm explainable recommendation by leveraging reviews and images":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "price aware recommendation with graph convolutional networks")
  Member "toward building conversational recommender systems a contextual bandit approach":
    also connected through other hyperedges:
      (none)
  Member "towards deep conversational recommendations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dynamic knowledge routing network for target guided open domain conversation")
  Member "learning contextual bandits in a non stationary environment":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "contextual bandit based personalized recommendation with time varying user interests")
  Member "collaborative memory network for recommendation systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lightgcn simplifying and powering graph convolution network for recommendation")
  Member "explicit state tracking with semi supervisionfor neural dialogue generation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unifying knowledge graph learning and recommendation towards a better understanding of user preferences")
  Member "aspect aware latent factor model rating prediction with ratings and reviews":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "recommender systems based on generative adversarial networks a problem driven perspective")

Question: Please tell me which class the source paper 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)