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: "learning important features through propagating activation differences"
  Abstract: The purported "black box"' nature of neural networks is a barrier to adoption in applications where interpretability is essential. Here we present DeepLIFT (Deep Learning Important FeaTures), a method for decomposing the output prediction of a neural network on a specific input by backpropagating the contributions of all neurons in the network to every feature of the input. DeepLIFT compares the activation of each neuron to its 'reference activation' and assigns contribution scores according to the difference. By optionally giving separate consideration to positive and negative contributions, DeepLIFT can also reveal dependencies which are missed by other approaches. Scores can be computed efficiently in a single backward pass. We apply DeepLIFT to models trained on MNIST and simulated genomic data, and show significant advantages over gradient-based methods. A detailed video tutorial on the method is at this http URL and code is at this http URL.

Connected through hyperedge HE-1 (induced by source paper "importance driven deep learning system testing"):
  with members:
    - "input prioritization for testing neural networks"
    - "deepfault fault localization for deep neural networks"
    - "machine learning testing survey landscapes and horizons"
    - "formal security analysis of neural networks using symbolic intervals"
    - "symbolic execution for deep neural networks"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "explaining explanations axiomatic feature interactions for deep networks"):
  with members:
    - "proxy tasks and subjective measures can be misleading in evaluating explainable ai systems"
    - "segment integrated gradients better attributions through regions"
    - "on identifiability in transformers"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "learning pairwise interactions with bayesian neural networks"
    - "incorporating priors with feature attribution on text classification"
    - "learning explainable models using attribution priors"
    - "is attention interpretable"

Connected through hyperedge HE-3 (induced by source paper "explaining with counter visual attributes and examples"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "one pixel attack for fooling deep neural networks"
    - "interpreting adversarially trained convolutional neural networks"
    - "counterfactual visual explanations"
    - "grounding visual explanations"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "a large scale attribute dataset for zero shot learning"
    - "techniques for interpretable machine learning"

Connected through hyperedge HE-4 (induced by source paper "exploratory not explanatory counterfactual analysis of saliency maps for deep reinforcement learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "counterfactual states for atari agents via generative deep learning"
    - "free lunch saliency via attention in atari agents"
    - "explaining explanations in ai"
    - "towards interpretable reinforcement learning using attention augmented agents"
    - "toybox better atari environments for testing reinforcement learning agents"
    - "towards better interpretability in deep q networks"
    - "investigating human priors for playing video games"

Connected through hyperedge HE-5 (induced by source paper "explainer a visual analytics framework for interactive and explainable machine learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "speculative execution for guided visual analytics"
    - "gan lab understanding complex deep generative models using interactive visual experimentation"
    - "human centered tools for coping with imperfect algorithms during medical decision making"
    - "retainvis visual analytics with interpretable and interactive recurrent neural networks on electronic medical records"
    - "manifold a model agnostic framework for interpretation and diagnosis of machine learning models"
    - "rulematrix visualizing and understanding classifiers with rules"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"

Connected through hyperedge HE-6 (induced by source paper "understanding image captioning models beyond visualizing attention"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "attention is not not explanation"
    - "bertscore evaluating text generation with bert"
    - "rubi reducing unimodal biases in visual question answering"
    - "attention is not explanation"
    - "unmasking clever hans predictors and assessing what machines really learn"
    - "evaluating recurrent neural network explanations"
    - "sanity checks for saliency maps"

Connected through hyperedge HE-7 (induced by source paper "aggregating explanation methods for stable and robust explainability"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "fairwashing the risk of rationalization"
    - "fooling neural network interpretations via adversarial model manipulation"
    - "interpretable deep learning under fire"
    - "a human grounded evaluation benchmark for local explanations of machine learning"
    - "explaining image classifiers by counterfactual generation"
    - "sanity checks for saliency maps"
    - "smoothgrad removing noise by adding noise"

Connected through hyperedge HE-8 (induced by source paper "robust saliency maps with decoy enhanced saliency score"):
  with members:
    - "understanding impacts of high order loss approximations and features in deep learning interpretation"
    - "certifiably robust interpretation in deep learning"
    - "interpreting neural networks using flip points"
    - "counterfactual visual explanations"
    - "explaining image classifiers by counterfactual generation"
    - "sanity checks for saliency maps"
    - "learning to explain an information theoretic perspective on model interpretation"
    - "deeppink reproducible feature selection in deep neural 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)