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: "towards robust locally linear deep networks"
  Abstract: Deep networks realize complex mappings that are often understood by their locally linear behavior at or around points of interest. For example, we use the derivative of the mapping with respect to its inputs for sensitivity analysis, or to explain (obtain coordinate relevance for) a prediction. One key challenge is that such derivatives are themselves inherently unstable. In this paper, we propose a new learning problem to encourage deep networks to have stable derivatives over larger regions. While the problem is challenging in general, we focus on networks with piecewise linear activation functions. Our algorithm consists of an inference step that identifies a region around a point where linear approximation is provably stable, and an optimization step to expand such regions. We propose a novel relaxation to scale the algorithm to realistic models. We illustrate our method with residual and recurrent networks on image and sequence datasets.

Connected through hyperedge HE-1 (induced by source paper "flexible and context specific ai explainability a multidisciplinary approach"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "functional transparency for structured data a game theoretic approach"
    - "explaining a black box by using a deep variational information bottleneck approach"
    - "learning a sat solver from single bit supervision"
    - "safely entering the deep a review of verification and validation for machine learning and a challenge elicitation in the automotive industry"
    - "contextual explanation networks"
    - "smoothgrad removing noise by adding noise"
    - "a causal framework for explaining the predictions of black box sequence to sequence models"

Connected through hyperedge HE-2 (induced by source paper "on the in fidelity and sensitivity for explanations"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "certified adversarial robustness via randomized smoothing"
    - "gnn explainer a tool for post hoc explanation of graph neural networks"
    - "counterfactual visual explanations"
    - "interpreting black box predictions using fisher kernels"
    - "representer point selection for explaining deep neural networks"
    - "model agnostic supervised local explanations"
    - "interpretable deep learning under fire"

Connected through hyperedge HE-3 (induced by source paper "tight certificates of adversarial robustness for randomly smoothed classifiers"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "certified robustness to adversarial word substitutions"
    - "the logbarrier adversarial attack making effective use of decision boundary information"
    - "achieving verified robustness to symbol substitutions via interval bound propagation"
    - "scaling provable adversarial defenses"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "a dual approach to scalable verification of deep networks"
    - "training verified learners with learned verifiers"

Connected through hyperedge HE-4 (induced by source paper "a game theoretic approach to class wise selective rationalization"):
  with members:
    - "functional transparency for structured data a game theoretic approach"
    - "rethinking cooperative rationalization introspective extraction and complement control"
    - "towards robust interpretability with self explaining neural networks"
    - "l shapley and c shapley efficient model interpretation for structured data"
    - "learning to explain an information theoretic perspective on model interpretation"
    - "deriving machine attention from human rationales"
    - "inferring and executing programs for visual reasoning"
    - "a causal framework for explaining the predictions of black box sequence to sequence models"

Connected through hyperedge HE-5 (induced by source paper "understanding adversarial robustness the trade off between minimum and average margin"):
  with members:
    - "scaling provable adversarial defenses"
    - "sparse dnns with improved adversarial robustness"
    - "on the effectiveness of interval bound propagation for training verifiably robust models"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "gradient descent aligns the layers of deep linear networks"
    - "semidefinite relaxations for certifying robustness to adversarial examples"
    - "provable robustness of relu networks via maximization of linear regions"
    - "deep defense training dnns with improved adversarial robustness"

Connected through hyperedge HE-6 (induced by source paper "certifiably robust interpretation in deep learning"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "generalizing multistain immunohistochemistry tissue segmentation using one shot color deconvolution deep neural networks"
    - "decoupling direction and norm for efficient gradient based l2 adversarial attacks and defenses"
    - "certified adversarial robustness with additive noise"
    - "sanity checks for saliency maps"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "smoothgrad removing noise by adding noise"

Connected through hyperedge HE-7 (induced by source paper "locally constant networks"):
  with members:
    - "on the convergence of adam and beyond"
    - "moleculenet a benchmark for molecular machine learning"
    - "densely connected convolutional networks"
    - "predicting the future relevance of research institutions the winning solution of the kdd cup 2016"
    - "deep residual learning for image recognition"
    - "convolutional networks on graphs for learning molecular fingerprints"
    - "efficient non greedy optimization of decision trees"
    - "a system for induction of oblique decision trees"

Connected through hyperedge HE-8 (induced by source paper "empirical bounds on linear regions of deep rectifier networks"):
  with members:
    - "equivalent and approximate transformations of deep neural networks"
    - "on decision regions of narrow deep neural networks"
    - "tropical geometry of deep neural networks"
    - "neural networks should be wide enough to learn disconnected decision regions"
    - "resnet with one neuron hidden layers is a universal approximator"
    - "provable robustness of relu networks via maximization of linear regions"
    - "deep neural networks as 0 1 mixed integer linear programs a feasibility study"
    - "evaluating robustness of neural networks with mixed integer programming"

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)