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 "on the effectiveness of low rank approximations for collaborative filtering compared to neural networks"):
  Abstract: Even in times of deep learning, low-rank approximations by factorizing a matrix into user and item latent factors continue to be a method of choice for collaborative filtering tasks due to their great performance. While deep learning based approaches excel in hybrid recommender tasks where additional features for items, users or even context are available, their flexibility seems to rather impair the performance compared to low-rank approximations for pure collaborative filtering tasks where no additional features are used. Recent works propose hybrid models combining low-rank approximations and traditional deep neural architectures with promising results but fail to explain why neural networks alone are unsuitable for this task. In this work, we revisit the model and intuition behind low-rank approximation to point out its suitability for collaborative filtering tasks. In several experiments we compare the performance and behavior of models based on a deep neural network and low-rank approximation to examine the reasons for the low effectiveness of traditional deep neural networks. We conclude that the universal approximation capabilities of traditional deep neural networks severely impair the determination of suitable latent vectors, leading to a worse performance compared to low-rank approximations.

Includes members:
  Member "deep learning based recommender system a survey and new perspectives":
    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 "outer product based neural collaborative filtering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sequential recommendation with dual side neighbor based collaborative relation modeling")
  Member "neural arithmetic logic units":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stealing black box functionality using the deep neural tree architecture")
  Member "neural collaborative ranking":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "neural collaborative filtering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "advances in collaborative filtering and ranking")
  Member "deepfm a factorization machine based neural network for ctr prediction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "neural network matrix factorization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "parameter space factorization for zero shot learning across tasks and languages")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)