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: "contextual explanation networks"
  Abstract: We introduce contextual explanation networks (CENs)---a class of models that learn to predict by generating and leveraging intermediate explanations. CENs are deep networks that generate parameters for context-specific probabilistic graphical models which are further used for prediction and play the role of explanations. Contrary to the existing post-hoc model-explanation tools, CENs learn to predict and to explain jointly. Our approach offers two major advantages: (i) for each prediction, valid instance-specific explanations are generated with no computational overhead and (ii) prediction via explanation acts as a regularization and boosts performance in low-resource settings. We prove that local approximations to the decision boundary of our networks are consistent with the generated explanations. Our results on image and text classification and survival analysis tasks demonstrate that CENs are competitive with the state-of-the-art while offering additional insights behind each prediction, valuable for decision support.

Connected through hyperedge HE-1 (induced by source paper "regularizing reasons for outfit evaluation with gradient penalty"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "toward explainable fashion recommendation"
    - "explainable fashion recommendation a semantic attribute region guided approach"
    - "context aware visual compatibility prediction"
    - "regularizing black box models for improved interpretability"
    - "dressing as a whole outfit compatibility learning based on node wise graph neural networks"
    - "outfit compatibility prediction and diagnosis with multi layered comparison network"
    - "learning type aware embeddings for fashion compatibility"

Connected through hyperedge HE-2 (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"
    - "towards robust locally linear deep networks"
    - "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"
    - "smoothgrad removing noise by adding noise"
    - "a causal framework for explaining the predictions of black box sequence to sequence models"

Connected through hyperedge HE-3 (induced by source paper "grid saliency for context explanations of semantic segmentation"):
  with members:
    - "interpreting adversarial examples with attributes"
    - "fast scnn fast semantic segmentation network"
    - "on physical adversarial patches for object detection"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "context proposals for saliency detection"
    - "guided upsampling network for real time semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"

Connected through hyperedge HE-4 (induced by source paper "text generation with exemplar based adaptive decoding"):
  with members:
    - "retrieve and refine improved sequence generation models for dialogue"
    - "backpropagating through structured argmax using a spigot"
    - "generating diverse and accurate visual captions by comparative adversarial learning"
    - "rational recurrences"
    - "bottom up abstractive summarization"
    - "learning joint semantic parsers from disjoint data"
    - "learning neural templates for text generation"
    - "contextual parameter generation for universal neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "interpretability beyond classification output semantic bottleneck networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "unified perceptual parsing for scene understanding"
    - "deep learning for case based reasoning through prototypes a neural network that explains its predictions"
    - "network dissection quantifying interpretability of deep visual representations"
    - "axiomatic attribution for deep networks"
    - "counterfactual explanations without opening the black box automated decisions and the gdpr"
    - "learning important features through propagating activation differences"
    - "visualizing deep neural network decisions prediction difference analysis"

Connected through hyperedge HE-6 (induced by source paper "functional transparency for structured data a game theoretic approach"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "game theoretic interpretability for temporal modeling"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "junction tree variational autoencoder for molecular graph generation"
    - "on the robustness of interpretability methods"
    - "deriving neural architectures from sequence and graph kernels"
    - "a causal framework for explaining the predictions of black box sequence to sequence models"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "regularizing black box models for improved interpretability"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "functional transparency for structured data a game theoretic approach"
    - "model agnostic supervised local explanations"
    - "sanity checks for saliency maps"
    - "on the robustness of interpretability methods"
    - "smoothgrad removing noise by adding noise"
    - "axiomatic attribution for deep networks"
    - "a unified approach to interpreting model predictions"

Connected through hyperedge HE-8 (induced by source paper "regularizing black box models for improved interpretability hill 2019 version"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "functional transparency for structured data a game theoretic approach"
    - "model agnostic supervised local explanations"
    - "sanity checks for saliency maps"
    - "on the robustness of interpretability methods"
    - "smoothgrad removing noise by adding noise"
    - "axiomatic attribution for deep networks"
    - "a unified approach to interpreting model predictions"

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)