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 "identify susceptible locations in medical records via adversarial attacks on deep predictive models"):
  Abstract: The surging availability of electronic medical records (EHR) leads to increased research interests in medical predictive modeling. Recently many deep learning based predicted models are also developed for EHR data and demonstrated impressive performance. However, a series of recent studies showed that these deep models are not safe: they suffer from certain vulnerabilities. In short, a well-trained deep network can be extremely sensitive to inputs with negligible changes. These inputs are referred to as adversarial examples. In the context of medical informatics, such attacks could alter the result of a high performance deep predictive model by slightly perturbing a patient's medical records. Such instability not only reflects the weakness of deep architectures, more importantly, it offers guide on detecting susceptible parts on the inputs. In this paper, we propose an efficient and effective framework that learns a time-preferential minimum attack targeting the LSTM model with EHR inputs, and we leverage this attack strategy to screen medical records of patients and identify susceptible events and measurements. The efficient screening procedure can assist decision makers to pay extra attentions to the locations that can cause severe consequence if not measured correctly. We conduct extensive empirical studies on a real-world urgent care cohort and demonstrate the effectiveness of the proposed screening approach.

Includes members:
  Member "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "secnlp a survey of embeddings in clinical natural language processing")
  Member "learning to evade static pe machine learning malware models via reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "automatic generation of adversarial examples for interpreting malware classifiers")
  Member "scalable and accurate deep learning for electronic health records":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "zimm a deep learning model for long term and blurry relapses with non clinical claims data")
  Member "analysis of universal adversarial perturbations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")
  Member "generating adversarial malware examples for black box attacks based on gan":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "automatic generation of adversarial examples for interpreting malware classifiers")
  Member "adversarial examples for evaluating reading comprehension systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a study of the tasks and models in machine reading comprehension")
  Member "generating natural adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "robustness verification for transformers")
  Member "crafting adversarial input sequences for recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "robustness verification for transformers")

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)