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: "examining cnn representations with respect to dataset bias"
  Abstract: Given a pre-trained CNN without any testing samples, this paper proposes a simple yet effective method to diagnose feature representations of the CNN. We aim to discover representation flaws caused by potential dataset bias. More specifically, when the CNN is trained to estimate image attributes, we mine latent relationships between representations of different attributes inside the CNN. Then, we compare the mined attribute relationships with ground-truth attribute relationships to discover the CNN's blind spots and failure modes due to dataset bias. In fact, representation flaws caused by dataset bias cannot be examined by conventional evaluation strategies based on testing images, because testing images may also have a similar bias. Experiments have demonstrated the effectiveness of our method.

Connected through hyperedge HE-1 (induced by source paper "deceptive ai explanations creation and detection"):
  with members:
    - "human grounded evaluations of explanation methods for text classification"
    - "a survey on bias and fairness in machine learning"
    - "how do i fool you manipulating user trust via misleading black box explanations"
    - "how to manipulate cnns to make them lie the gradcam case"
    - "generating sentiment preserving fake online reviews using neural language models and their human and machine based detection"
    - "how model accuracy and explanation fidelity influence user trust"
    - "perturbation sensitivity analysis to detect unintended model biases"
    - "rise randomized input sampling for explanation of black box models"

Connected through hyperedge HE-2 (induced by source paper "a multidisciplinary survey and framework for design and evaluation of explainable ai systems"):
  with members:
    - "fairsight visual analytics for fairness in decision making"
    - "predicting model failure using saliency maps in autonomous driving systems"
    - "human grounded evaluations of explanation methods for text classification"
    - "quantifying interpretability and trust in machine learning systems"
    - "summit scaling deep learning interpretability by visualizing activation and attribution summarizations"
    - "explainable artificial intelligence xai concepts taxonomies opportunities and challenges toward responsible ai"
    - "open issues in combating fake news interpretability as an opportunity"
    - "a survey on bias and fairness in machine learning"

Connected through hyperedge HE-3 (induced by source paper "exploring uncertainty measures for image caption embedding and retrieval task"):
  with members:
    - "a closer look at few shot classification"
    - "exploring uncertainty in conditional multi modal retrieval systems"
    - "quantifying uncertainties in natural language processing tasks"
    - "recipeqa a challenge dataset for multimodal comprehension of cooking recipes"
    - "object hallucination in image captioning"
    - "anomaly machine component detection by deep generative model with unregularized score"
    - "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"

Connected through hyperedge HE-4 (induced by source paper "knowledge consistency between neural networks and beyond"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "similarity of neural network representations revisited"
    - "quantifying layerwise information discarding of neural networks"
    - "towards a unified evaluation of explanation methods without ground truth"
    - "utility analysis of network architectures for 3d point cloud processing"
    - "stiffness a new perspective on generalization in neural networks"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "towards understanding learning representations to what extent do different neural networks learn the same representation"

Connected through hyperedge HE-5 (induced by source paper "quantifying layerwise information discarding of neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "stiffness a new perspective on generalization in neural networks"
    - "towards explanation of dnn based prediction with guided feature inversion"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "dynamic routing between capsules"
    - "a unified approach to interpreting model predictions"

Connected through hyperedge HE-6 (induced by source paper "rectified decision trees towards interpretability compression and empirical soundness"):
  with members:
    - "interpreting deep visual representations via network dissection"
    - "deep forest towards an alternative to deep neural networks"
    - "dynamic routing between capsules"
    - "distilling a neural network into a soft decision tree"
    - "autoencoder by forest"
    - "channel pruning for accelerating very deep neural networks"
    - "xgboost a scalable tree boosting system"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"

Connected through hyperedge HE-7 (induced by source paper "directing dnns attention for facial attribution classification using gradient weighted class activation mapping"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "attention branch network learning of attention mechanism for visual explanation"
    - "tell me where to look guided attention inference network"
    - "unitbox an advanced object detection network"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-8 (induced by source paper "visual interpretability for deep learning a survey"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "interpreting cnns via decision trees"
    - "network dissection quantifying interpretability of deep visual representations"
    - "towards interpretable r cnn by unfolding latent structures"
    - "interactively transferring cnn patterns for part localization"
    - "explaining the unexplained a class enhanced attentive response clear approach to understanding deep neural networks"
    - "interpreting cnn knowledge via an explanatory graph"
    - "dynamic routing between capsules"

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)