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: "deep learning for case based reasoning through prototypes a neural network that explains its predictions"
  Abstract: Deep neural networks are widely used for classification. These deep models often suffer from a lack of interpretability -- they are particularly difficult to understand because of their non-linear nature. As a result, neural networks are often treated as "black box" models, and in the past, have been trained purely to optimize the accuracy of predictions. In this work, we create a novel network architecture for deep learning that naturally explains its own reasoning for each prediction. This architecture contains an autoencoder and a special prototype layer, where each unit of that layer stores a weight vector that resembles an encoded training input. The encoder of the autoencoder allows us to do comparisons within the latent space, while the decoder allows us to visualize the learned prototypes. The training objective has four terms: an accuracy term, a term that encourages every prototype to be similar to at least one encoded input, a term that encourages every encoded input to be close to at least one prototype, and a term that encourages faithful reconstruction by the autoencoder. The distances computed in the prototype layer are used as part of the classification process. Since the prototypes are learned during training, the learned network naturally comes with explanations for each prediction, and the explanations are loyal to what the network actually computes.

Connected through hyperedge HE-1 (induced by source paper "clara clinical report auto completion"):
  with members:
    - "clinically accurate chest x ray report generation"
    - "knowledge driven encode retrieve paraphrase for medical image report generation"
    - "neural baby talk"
    - "a neural compositional paradigm for image captioning"
    - "personalized language model for query auto completion"
    - "on the automatic generation of medical imaging reports"
    - "hybrid retrieval generation reinforced agent for medical image report generation"
    - "producing radiologist quality reports for interpretable artificial intelligence"

Connected through hyperedge HE-2 (induced by source paper "concept whitening for interpretable image recognition"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "the convolutional tsetlin machine"
    - "educe explaining model decisions through unsupervised concepts extraction"
    - "saccader improving accuracy of hard attention models for vision"
    - "cheap orthogonal constraints in neural networks a simple parametrization of the orthogonal and unitary group"
    - "on concept based explanations in deep neural networks"
    - "iterative normalization beyond standardization towards efficient whitening"
    - "this looks like that deep learning for interpretable image recognition"

Connected through hyperedge HE-3 (induced by source paper "bayes probe distribution guided sampling for prediction level sets"):
  with members:
    - "fooling lime and shap adversarial attacks on post hoc explanation methods"
    - "out of distribution detection in classifiers via generation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "pyro deep universal probabilistic programming"
    - "this looks like that deep learning for interpretable image recognition"
    - "sim to real learning agile locomotion for quadruped robots"
    - "certified adversarial robustness with additive noise"
    - "sanity checks for saliency maps"

Connected through hyperedge HE-4 (induced by source paper "black box explanation by learning image exemplars in the latent feature space"):
  with members:
    - "enhancing the robustness of deep neural networks by boundary conditional gan"
    - "local rule based explanations of black box decision systems"
    - "this looks like that deep learning for interpretable image recognition"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "distilling a neural network into a soft decision tree"
    - "not just a black box learning important features through propagating activation differences"
    - "why should i trust you explaining the predictions of any classifier"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "understanding artificial intelligence ethics and safety"):
  with members:
    - "a roadmap toward the resilient internet of things for cyber physical systems"
    - "a grounded interaction protocol for explainable artificial intelligence"
    - "automated rationale generation a technique for explainable ai and its effects on human perceptions"
    - "model cards for model reporting"
    - "a framework for understanding unintended consequences of machine learning"
    - "spectrum data poisoning with adversarial deep learning"
    - "explaining explanations in ai"
    - "problem formulation and fairness"

Connected through hyperedge HE-6 (induced by source paper "protoattend attention based prototypical learning"):
  with members:
    - "on the robustness of deep k nearest neighbors"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "representer point selection for explaining deep neural networks"
    - "this looks like that deep learning for interpretable image recognition"
    - "deep anomaly detection with outlier exposure"
    - "catboost gradient boosting with categorical features support"
    - "incremental few shot learning with attention attractor networks"
    - "attention based ensemble for deep metric learning"

Connected through hyperedge HE-7 (induced by source paper "interpretable image recognition with hierarchical prototypes"):
  with members:
    - "tree cnn a deep convolutional neural network for lifelong learning"
    - "this looks like that deep learning for interpretable image recognition"
    - "branchconnect image categorization with learned branch connections"
    - "does your model know the digit 6 is not a cat a less biased evaluation of outlier detectors"
    - "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem"
    - "network dissection quantifying interpretability of deep visual representations"
    - "axiomatic attribution for deep networks"
    - "b cnn branch convolutional neural network for hierarchical classification"

Connected through hyperedge HE-8 (induced by source paper "personalized explanation in machine learning a conceptualization"):
  with members:
    - "explanation methods in deep learning users values concerns and challenges"
    - "interpretable to whom a role based model for analyzing interpretable machine learning systems"
    - "explainable recommendation a survey and new perspectives"
    - "technical privacy metrics a systematic survey"
    - "methods for interpreting and understanding deep neural networks"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "visually explainable recommendation"
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"

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.AI(Artificial Intelligence)