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 "fibinet combining feature importance and bilinear feature interaction for click through rate prediction"):
  Abstract: Advertising and feed ranking are essential to many Internet companies such as Facebook and Sina Weibo. Among many real-world advertising and feed ranking systems, click through rate (CTR) prediction plays a central role. There are many proposed models in this field such as logistic regression, tree based models, factorization machine based models and deep learning based CTR models. However, many current works calculate the feature interactions in a simple way such as Hadamard product and inner product and they care less about the importance of features. In this paper, a new model named FiBiNET as an abbreviation for Feature Importance and Bilinear feature Interaction NETwork is proposed to dynamically learn the feature importance and fine-grained feature interactions. On the one hand, the FiBiNET can dynamically learn the importance of features via the Squeeze-Excitation network (SENET) mechanism; on the other hand, it is able to effectively learn the feature interactions via bilinear function. We conduct extensive experiments on two real-world datasets and show that our shallow model outperforms other shallow models such as factorization machine(FM) and field-aware factorization machine(FFM). In order to improve performance further, we combine a classical deep neural network(DNN) component with the shallow model to be a deep model. The deep FiBiNET consistently outperforms the other state-of-the-art deep models such as DeepFM and extreme deep factorization machine(XdeepFM).

Includes members:
  Member "skin lesion classification with ensemble of squeeze and excitation networks and semi supervised learning":
    also connected through other hyperedges:
      (none)
  Member "weakly supervised deep learning for thoracic disease classification and localization on chest x rays":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "when labelled data hurts deep semi supervised classification with the graph 1 laplacian")
  Member "xdeepfm combining explicit and implicit feature interactions for recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cross gcn enhancing graph convolutional network with k order feature interactions")
  Member "learning what and where to attend":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "stochastic region pooling make attention more expressive")
  Member "recalibrating fully convolutional networks with spatial and channel squeeze excitation blocks":
    also connected through other hyperedges:
      (none)
  Member "neural factorization machines for sparse predictive analytics":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "parameter efficient transfer from sequential behaviors for user profiling and recommendation")
  Member "field aware factorization machines in a real world online advertising system":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "metaselector meta learning for recommendation with user level adaptive model selection")
  Member "deep cross network for ad click predictions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cross gcn enhancing graph convolutional network with k order feature interactions")

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)