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 "towards neural mixture recommender for long range dependent user sequences"):
  Abstract: Understanding temporal dynamics has proved to be highly valuable for accurate recommendation. Sequential recommenders have been successful in modeling the dynamics of users and items over time. However, while different model architectures excel at capturing various temporal ranges or dynamics, distinct application contexts require adapting to diverse behaviors.    In this paper we examine how to build a model that can make use of different temporal ranges and dynamics depending on the request context. We begin with the analysis of an anonymized Youtube dataset comprising millions of user sequences. We quantify the degree of long-range dependence in these sequences and demonstrate that both short-term and long-term dependent behavioral patterns co-exist. We then propose a neural Multi-temporal-range Mixture Model (M3) as a tailored solution to deal with both short-term and long-term dependencies. Our approach employs a mixture of models, each with a different temporal range. These models are combined by a learned gating mechanism capable of exerting different model combinations given different contextual information. In empirical evaluations on a public dataset and our own anonymized YouTube dataset, M3 consistently outperforms state-of-the-art sequential recommendation methods.

Includes members:
  Member "ranking distillation learning compact ranking models with high performance for recommender system":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "aibench an agile domain specific benchmarking methodology and an ai benchmark suite")
  Member "a simple convolutional generative network for next item recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "parameter efficient transfer from sequential behaviors for user profiling and recommendation")
  Member "top k off policy correction for a reinforce recommender system":
    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 "personalized top n sequential recommendation via convolutional sequence embedding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "advances in collaborative filtering and ranking")
  Member "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "blank language models")
  Member "a brand level ranking system with the customized attention gru model":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "exact k recommendation via maximal clique optimization")
  Member "outrageously large neural networks the sparsely gated mixture of experts layer":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "attention over parameters for dialogue systems")
  Member "session aware information embedding for e commerce product recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep learning for sequential recommendation algorithms influential factors and evaluations")

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.LG(Machine Learning)