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 "a troubling analysis of reproducibility and progress in recommender systems research"):
  Abstract: The design of algorithms that generate personalized ranked item lists is a central topic of research in the field of recommender systems. In the past few years, in particular, approaches based on deep learning (neural) techniques have become dominant in the literature. For all of them, substantial progress over the state-of-the-art is claimed. However, indications exist of certain problems in today's research practice, e.g., with respect to the choice and optimization of the baselines used for comparison, raising questions about the published claims. In order to obtain a better understanding of the actual progress, we have tried to reproduce recent results in the area of neural recommendation approaches based on collaborative filtering. The worrying outcome of the analysis of these recent works-all were published at prestigious scientific conferences between 2015 and 2018-is that 11 out of the 12 reproducible neural approaches can be outperformed by conceptually simple methods, e.g., based on the nearest-neighbor heuristics. None of the computationally complex neural methods was actually consistently better than already existing learning-based techniques, e.g., using matrix factorization or linear models. In our analysis, we discuss common issues in today's research practice, which, despite the many papers that are published on the topic, have apparently led the field to a certain level of stagnation.

Includes members:
  Member "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "selective weak supervision for neural information retrieval")
  Member "embarrassingly shallow autoencoders for sparse data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "recvae a new variational autoencoder for top n recommendations with implicit feedback")
  Member "on the difficulty of evaluating baselines a study on recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "accelerated learning from recommender systems using multi armed bandit")
  Member "are we really making much progress a worrying analysis of recent neural recommendation approaches":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "simple and effective graph autoencoders with one hop linear models")
  Member "attentive neural architecture incorporating song features for music recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep learning for sequential recommendation algorithms influential factors and evaluations")
  Member "outer product based neural collaborative filtering":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sequential recommendation with dual side neighbor based collaborative relation modeling")
  Member "collaborative memory network for recommendation systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lightgcn simplifying and powering graph convolution network for recommendation")
  Member "spectral collaborative filtering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "revisiting graph based collaborative filtering a linear residual graph convolutional network approach")

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)