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 "model based reinforcement learning for whole chain recommendations"):
  Abstract: With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in developing RL-based recommender systems. In practical recommendation sessions, users will sequentially access multiple scenarios, such as the entrance pages and the item detail pages, and each scenario has its own recommendation strategy. However, the majority of existing RL-based recommender systems focus on optimizing one strategy for all scenarios or separately optimizing each strategy, which could lead to sub-optimal overall performance. In this paper, we study the recommendation problem with multiple (consecutive) scenarios, i.e., whole-chain recommendations. We propose a multi-agent reinforcement learning based approach (DeepChain), which can capture the sequential correlation among different scenarios and jointly optimize multiple recommendation strategies. To be specific, all recommender agents share the same memory of users' historical behaviors, and they work collaboratively to maximize the overall reward of a session. Note that optimizing multiple recommendation strategies jointly faces two challenges in existing model-free RL model \cite{feng2018learning}- (i) it requires huge amounts of user behavior data, and (ii) the distribution of reward (users' feedback) are extremely unbalanced. In this paper, we introduce model-based reinforcement learning techniques to reduce the training data requirement and execute more accurate strategy updates. The experimental results based on a real e-commerce platform demonstrate the effectiveness of the proposed framework.

Includes members:
  Member "toward simulating environments in reinforcement learning based recommendations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "recsim a configurable simulation platform for recommender systems")
  Member "learning to collaborate multi scenario ranking via multi agent reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (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-4 (induced by source paper "developing multi task recommendations with long term rewards via policy distilled reinforcement learning")
  Member "reinforcement learning for online information seeking":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reinforcement learning applications")
  Member "reinforcement learning based recommender system using biclustering technique":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "jointly learning to recommend and advertise")
  Member "top k off policy correction for a reinforce recommender system":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "developing multi task recommendations with long term rewards via policy distilled reinforcement learning")
  Member "deep reinforcement learning based recommendation with explicit user item interactions modeling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "heterogeneous relational reasoning in knowledge graphs with reinforcement learning")

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)