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: "investigating decision boundaries of trained neural networks"
  Abstract: Deep learning models have been the subject of study from various perspectives, for example, their training process, interpretation, generalization error, robustness to adversarial attacks, etc. A trained model is defined by its decision boundaries, and therefore, many of the studies about deep learning models speculate about the decision boundaries, and sometimes make simplifying assumptions about them. So far, finding exact points on the decision boundaries of trained deep models has been considered an intractable problem. Here, we compute exact points on the decision boundaries of these models and provide mathematical tools to investigate the surfaces that define the decision boundaries. Through numerical results, we confirm that some of the speculations about the decision boundaries are accurate, some of the computational methods can be improved, and some of the simplifying assumptions may be unreliable, for models with nonlinear activation functions. We advocate for verification of simplifying assumptions and approximation methods, wherever they are used. Finally, we demonstrate that the computational practices used for finding adversarial examples can be improved and computing the closest point on the decision boundary reveals the weakest vulnerability of a model against adversarial attack.

Connected through hyperedge HE-1 (induced by source paper "auditing and debugging deep learning models via decision boundaries individual level and group level analysis"):
  with members:
    - "an evaluation of the human interpretability of explanation"
    - "fooling lime and shap adversarial attacks on post hoc explanation methods"
    - "measurable counterfactual local explanations for any classifier"
    - "how can we fool lime and shap adversarial attacks on post hoc explanation methods"
    - "on the accuracy of influence functions for measuring group effects"
    - "how do i fool you manipulating user trust via misleading black box explanations"
    - "refining the structure of neural networks using matrix conditioning"
    - "interpreting neural networks using flip points"

Connected through hyperedge HE-2 (induced by source paper "characterizing the decision boundary of deep neural networks"):
  with members:
    - "deep adversarial social recommendation"
    - "learning hierarchical discourse level structure for fake news detection"
    - "deep adversarial network alignment"
    - "on the decision boundaries of deep neural networks a tropical geometry perspective"
    - "adversarial attacks and defenses in images graphs and text a review"
    - "pytorch an imperative style high performance deep learning library"
    - "adversarial attacks on deep learning models in natural language processing a survey"
    - "how complex is your classification problem a survey on measuring classification complexity"

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)