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 "deep reinforcement learning for online advertising in recommender systems"):
  Abstract: With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in utilizing RL for online advertising in recommendation platforms (e.g. e-commerce and news feed sites). However, most RL-based advertising algorithms focus on solely optimizing the revenue of ads while ignoring possible negative influence of ads on user experience of recommended items (products, articles and videos). Developing an optimal advertising algorithm in recommendations faces immense challenges because interpolating ads improperly or too frequently may decrease user experience, while interpolating fewer ads will reduce the advertising revenue. Thus, in this paper, we propose a novel advertising strategy for the rec/ads trade-off. To be specific, we develop a reinforcement learning based framework that can continuously update its advertising strategies and maximize reward in the long run. Given a recommendation list, we design a novel Deep Q-network architecture that can determine three internally related tasks jointly, i.e., (i) whether to interpolate an ad or not in the recommendation list, and if yes, (ii) the optimal ad and (iii) the optimal location to interpolate. The experimental results based on real-world data demonstrate the effectiveness of the proposed framework.

Includes members:
  Member "model based reinforcement learning for whole chain recommendations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep reinforcement learning for search recommendation and online advertising a survey by xiangyu zhao long xia jiliang tang and dawei yin with martin vesely as coordinator")
  Member "toward simulating environments in reinforcement learning based recommendations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "recsim a configurable simulation platform for recommender systems")
  Member "deep reinforcement learning for search recommendation and online advertising a survey by xiangyu zhao long xia jiliang tang and dawei yin with martin vesely as coordinator":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the big three a methodology to increase data science roi by answering the questions companies care about")
  Member "learning to collaborate multi scenario ranking via multi agent reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "jointly learning to recommend and advertise")
  Member "recommendations with negative feedback via pairwise deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "developing multi task recommendations with long term rewards via policy distilled reinforcement learning")
  Member "deep reinforcement learning for page wise recommendations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "developing multi task recommendations with long term rewards via policy distilled reinforcement learning")
  Member "a multi agent reinforcement learning method for impression allocation in online display advertising":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "jointly learning to recommend and advertise")
  Member "learning to advertise with adaptive exposure via constrained two level reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "jointly learning to recommend and advertise")

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)