human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "poba ga perturbation optimized black box adversarial attacks via genetic algorithm"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "adversarial attacks on face detectors using neural net based constrained optimization"
    - "security analysis and enhancement of model compressed deep learning systems under adversarial attacks"
    - "adversarial generative nets neural network attacks on state of the art face recognition"
    - "asp a fast adversarial attack example generation framework based on adversarial saliency prediction"
    - "black box adversarial attacks with limited queries and information"

Connected through hyperedge HE-2 (induced by source paper "universal adversarial perturbation for text classification"):
  with members:
    - "textbugger generating adversarial text against real world applications"
    - "deep text classification can be fooled"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "universal adversarial training"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "hotflip white box adversarial examples for text classification"
    - "fast feature fool a data independent approach to universal adversarial perturbations"
    - "analysis of universal adversarial perturbations"

Connected through hyperedge HE-3 (induced by source paper "adacare explainable clinical health status representation learning via scale adaptive feature extraction and recalibration"):
  with members:
    - "concare personalized clinical feature embedding via capturing the healthcare context"
    - "mime multilevel medical embedding of electronic health records for predictive healthcare"
    - "learning the joint representation of heterogeneous temporal events for clinical endpoint prediction"
    - "dipole diagnosis prediction in healthcare via attention based bidirectional recurrent neural networks"
    - "wavenet a generative model for raw audio"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "multi scale context aggregation by dilated convolutions"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "is robustness the cost of accuracy a comprehensive study on the robustness of 18 deep image classification models"):
  with members:
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "spatially transformed adversarial examples"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "on the limitation of local intrinsic dimensionality for characterizing the subspaces of adversarial examples"
    - "generating adversarial examples with adversarial networks"
    - "query efficient hard label black box attack an optimization based approach"
    - "audio adversarial examples targeted attacks on speech to text"
    - "on the limitation of magnet defense against l_1 based adversarial examples"

Connected through hyperedge HE-5 (induced by source paper "defend deep neural networks against adversarial examples via fixed anddynamic quantized activation functions"):
  with members:
    - "stochastic activation pruning for robust adversarial defense"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "l2 nonexpansive neural networks"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "deflecting adversarial attacks with pixel deflection"
    - "sok security and privacy in machine learning"
    - "audio adversarial examples targeted attacks on speech to text"
    - "certified defenses against adversarial examples"

Connected through hyperedge HE-6 (induced by source paper "parametric noise injection trainable randomness to improve deep neural network robustness against adversarial attack"):
  with members:
    - "l2 nonexpansive neural networks"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "on the robustness of the cvpr 2018 white box adversarial example defenses"
    - "audio adversarial examples targeted attacks on speech to text"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "towards robust neural networks via random self ensemble"
    - "mitigating adversarial effects through randomization"

Connected through hyperedge HE-7 (induced by source paper "query efficient black box attack by active learning"):
  with members:
    - "autozoom autoencoder based zeroth order optimization method for attacking black box neural networks"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "audio adversarial examples targeted attacks on speech to text"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "robust physical world attacks on deep learning models"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "learning universal adversarial perturbations with generative models"
    - "countering adversarial images using input transformations"

Question: Please tell me which class the center node 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)