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 "sequential recommender systems challenges progress and prospects"):
  Abstract: The emerging topic of sequential recommender systems has attracted increasing attention in recent years.Different from the conventional recommender systems including collaborative filtering and content-based filtering, SRSs try to understand and model the sequential user behaviors, the interactions between users and items, and the evolution of users preferences and item popularity over time. SRSs involve the above aspects for more precise characterization of user contexts, intent and goals, and item consumption trend, leading to more accurate, customized and dynamic recommendations.In this paper, we provide a systematic review on SRSs.We first present the characteristics of SRSs, and then summarize and categorize the key challenges in this research area, followed by the corresponding research progress consisting of the most recent and representative developments on this topic.Finally, we discuss the important research directions in this vibrant area.

Includes members:
  Member "session based recommendation with graph neural network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "towards neural mixture recommender for long range dependent user sequences":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "future data helps training modeling future contexts for session based recommendation")
  Member "a survey on session based recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "session based recommender systems for action selection in gui test generation")
  Member "recommendation through mixtures of heterogeneous item relationships":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "relational collaborative filtering modeling multiple item relations for recommendation")
  Member "a simple convolutional generative network for next item recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "parameter efficient transfer from sequential behaviors for user profiling and recommendation")
  Member "personalized top n sequential recommendation via convolutional sequence embedding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "advances in collaborative filtering and ranking")
  Member "personalizing session based recommendations with hierarchical recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "fusing similarity models with markov chains for sparse sequential recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sequential recommendation with dual side neighbor based collaborative relation modeling")

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)