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: "local explanation methods for deep neural networks lack sensitivity to parameter values"
  Abstract: Explaining the output of a complicated machine learning model like a deep neural network (DNN) is a central challenge in machine learning. Several proposed local explanation methods address this issue by identifying what dimensions of a single input are most responsible for a DNN's output. The goal of this work is to assess the sensitivity of local explanations to DNN parameter values. Somewhat surprisingly, we find that DNNs with randomly-initialized weights produce explanations that are both visually and quantitatively similar to those produced by DNNs with learned weights. Our conjecture is that this phenomenon occurs because these explanations are dominated by the lower level features of a DNN, and that a DNN's architecture provides a strong prior which significantly affects the representations learned at these lower layers. NOTE: This work is now subsumed by our recent manuscript, Sanity Checks for Saliency Maps (to appear NIPS 2018), where we expand on findings and address concerns raised in Sundararajan et. al. (2018).

Connected through hyperedge HE-1 (induced by source paper "tedl a text encryption method based on deep learning"):
  with members:
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "lightweight efficient multi keyword ranked search over encrypted cloud data using dual word embeddings"
    - "recent trends in deep learning based natural language processing"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "membership inference attacks against machine learning models"
    - "towards biologically plausible deep learning"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-2 (induced by source paper "on the benefits of attributional robustness"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "attention based dropout layer for weakly supervised object localization"
    - "robust attribution regularization"
    - "interpretable and fine grained visual explanations for convolutional neural networks"
    - "theoretically principled trade off between robustness and accuracy"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "defense against adversarial attacks using feature scattering based adversarial training"
    - "harnessing the vulnerability of latent layers in adversarially trained models"

Connected through hyperedge HE-3 (induced by source paper "finding and visualizing weaknesses of deep reinforcement learning agents"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "adversarial policies attacking deep reinforcement learning"
    - "establishing appropriate trust via critical states"
    - "rigorous agent evaluation an adversarial approach to uncover catastrophic failures"
    - "sanity checks for saliency maps"
    - "understanding regularization to visualize convolutional neural networks"
    - "evaluating feature importance estimates"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"

Connected through hyperedge HE-4 (induced by source paper "free lunch saliency via attention in atari agents"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "atari head atari human eye tracking and demonstration dataset"
    - "visual rationalizations in deep reinforcement learning for atari games"
    - "reinforcement learning with attention that works a self supervised approach"
    - "towards interpretable reinforcement learning using attention augmented agents"
    - "agil learning attention from human for visuomotor tasks"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "an introduction to deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "score cam improved visual explanations via score weighted class activation mapping"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "interpretable and fine grained visual explanations for convolutional neural networks"
    - "smooth grad cam an enhanced inference level visualization technique for deep convolutional neural network models"
    - "u cam visual explanation using uncertainty based class activation maps"
    - "towards explanation of dnn based prediction with guided feature inversion"
    - "techniques for interpretable machine learning"
    - "explaining image classifiers by counterfactual generation"
    - "rise randomized input sampling for explanation of black box models"

Connected through hyperedge HE-6 (induced by source paper "interpretable and fine grained visual explanations for convolutional neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "towards explanation of dnn based prediction with guided feature inversion"
    - "visual interpretability for deep learning a survey"
    - "regional multi scale approach for visually pleasing explanations of deep neural networks"
    - "techniques for interpretable machine learning"
    - "a theoretical explanation for perplexing behaviors of backpropagation based visualizations"
    - "explaining image classifiers by counterfactual generation"
    - "rise randomized input sampling for explanation of black box models"

Connected through hyperedge HE-7 (induced by source paper "a benchmark for interpretability methods in deep neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "do better imagenet models transfer better"
    - "revisiting the importance of individual units in cnns via ablation"
    - "rise randomized input sampling for explanation of black box models"
    - "sanity checks for saliency maps"
    - "smoothgrad removing noise by adding noise"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "axiomatic attribution for deep networks"

Connected through hyperedge HE-8 (induced by source paper "j net randomly weighted u net for audio source separation"):
  with members:
    - "multiresunet rethinking the u net architecture for multimodal biomedical image segmentation"
    - "progressive image deraining networks a better and simpler baseline"
    - "convolutional recurrent neural network based progressive learning for monaural speech enhancement"
    - "audio denoising with deep network priors"
    - "randomly weighted cnns for music audio classification"
    - "intriguing properties of randomly weighted networks generalizing while learning next to nothing"
    - "deep image prior"
    - "unpaired image to image translation using cycle consistent adversarial networks"

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.CV(Computer Vision and Pattern Recognition)