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 "safe a neural survival analysis model for fraud early detection"):
  Abstract: Many online platforms have deployed anti-fraud systems to detect and prevent fraudulent activities. However, there is usually a gap between the time that a user commits a fraudulent action and the time that the user is suspended by the platform. How to detect fraudsters in time is a challenging problem. Most of the existing approaches adopt classifiers to predict fraudsters given their activity sequences along time. The main drawback of classification models is that the prediction results between consecutive timestamps are often inconsistent. In this paper, we propose a survival analysis based fraud early detection model, SAFE, which maps dynamic user activities to survival probabilities that are guaranteed to be monotonically decreasing along time. SAFE adopts recurrent neural network (RNN) to handle user activity sequences and directly outputs hazard values at each timestamp, and then, survival probability derived from hazard values is deployed to achieve consistent predictions. Because we only observe the user suspended time instead of the fraudulent activity time in the training data, we revise the loss function of the regular survival model to achieve fraud early detection. Experimental results on two real world datasets demonstrate that SAFE outperforms both the survival analysis model and recurrent neural network model alone as well as state-of-the-art fraud early detection approaches.

Includes members:
  Member "a recurrent neural network survival model predicting web user return time":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep recurrent survival analysis")
  Member "false information on web and social media a survey":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "discover your social identity from what you tweet a content based approach")
  Member "deep learning for patient specific kidney graft survival analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "decoding pedestrian and automated vehicle interactions using immersive virtual reality and interpretable deep learning")
  Member "machine learning for survival analysis a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "survlime a method for explaining machine learning survival models")
  Member "spectrum based deep neural networks for fraud detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fraud detection in networks state of the art")
  Member "wikipedia vandal early detection from user behavior to user embedding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "stre self attentive edit quality prediction in wikipedia")
  Member "vews a wikipedia vandal early warning system":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "predicting dynamic embedding trajectory in temporal interaction networks")
  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.LG(Machine Learning)