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: "subset scanning over neural network activations"
  Abstract: This work views neural networks as data generating systems and applies anomalous pattern detection techniques on that data in order to detect when a network is processing an anomalous input. Detecting anomalies is a critical component for multiple machine learning problems including detecting adversarial noise. More broadly, this work is a step towards giving neural networks the ability to recognize an out-of-distribution sample. This is the first work to introduce "Subset Scanning" methods from the anomalous pattern detection domain to the task of detecting anomalous input of neural networks. Subset scanning treats the detection problem as a search for the most anomalous subset of node activations (i.e., highest scoring subset according to non-parametric scan statistics). Mathematical properties of these scoring functions allow the search to be completed in log-linear rather than exponential time while still guaranteeing the most anomalous subset of nodes in the network is identified for a given input. Quantitative results for detecting and characterizing adversarial noise are provided for CIFAR-10 images on a simple convolutional neural network. We observe an "interference" pattern where anomalous activations in shallow layers suppress the activation structure of the original image in deeper layers.

Connected through hyperedge HE-1 (induced by source paper "adversarial perturbations on the perceptual ball"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "understanding deep networks via extremal perturbations and smooth masks"
    - "interpretable and fine grained visual explanations for convolutional neural networks"
    - "interpretable counterfactual explanations guided by prototypes"
    - "universal adversarial perturbations against person re identification"
    - "the odds are odd a statistical test for detecting adversarial examples"
    - "counterfactual visual explanations"
    - "hardening deep neural networks via adversarial model cascades"

Connected through hyperedge HE-2 (induced by source paper "smooth grad cam an enhanced inference level visualization technique for deep convolutional neural network models"):
  with members:
    - "contextual explanation networks"
    - "smoothgrad removing noise by adding noise"
    - "axiomatic attribution for deep networks"
    - "learning important features through propagating activation differences"
    - "why should i trust you explaining the predictions of any classifier"
    - "visualizing deep convolutional neural networks using natural pre images"
    - "a new method to visualize deep neural networks"
    - "understanding neural networks through deep visualization"

Connected through hyperedge HE-3 (induced by source paper "a step towards exposing bias in trained deep convolutional neural network models"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "contextual explanation networks"
    - "smoothgrad removing noise by adding noise"
    - "axiomatic attribution for deep networks"
    - "learning important features through propagating activation differences"
    - "why should i trust you explaining the predictions of any classifier"
    - "a new method to visualize deep neural networks"
    - "learning deep features for discriminative localization"

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)