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: "towards interpretable r cnn by unfolding latent structures"
  Abstract: This paper first proposes a method of formulating model interpretability in visual understanding tasks based on the idea of unfolding latent structures. It then presents a case study in object detection using popular two-stage region-based convolutional network (i.e., R-CNN) detection systems. We focus on weakly-supervised extractive rationale generation, that is learning to unfold latent discriminative part configurations of object instances automatically and simultaneously in detection without using any supervision for part configurations. We utilize a top-down hierarchical and compositional grammar model embedded in a directed acyclic AND-OR Graph (AOG) to explore and unfold the space of latent part configurations of regions of interest (RoIs). We propose an AOGParsing operator to substitute the RoIPooling operator widely used in R-CNN. In detection, a bounding box is interpreted by the best parse tree derived from the AOG on-the-fly, which is treated as the qualitatively extractive rationale generated for interpreting detection. We propose a folding-unfolding method to train the AOG and convolutional networks end-to-end. In experiments, we build on R-FCN and test our method on the PASCAL VOC 2007 and 2012 datasets. We show that the method can unfold promising latent structures without hurting the performance.

Connected through hyperedge HE-1 (induced by source paper "chexplain enabling physicians to explore and understanddata driven ai enabled medical imaging analysis"):
  with members:
    - "outlining the design space of explainable intelligent systems for medical diagnosis"
    - "human centered tools for coping with imperfect algorithms during medical decision making"
    - "explanation methods in deep learning users values concerns and challenges"
    - "explainable cardiac pathology classification on cine mri with motion characterization by semi supervised learning of apparent flow"
    - "explanations based on the missing towards contrastive explanations with pertinent negatives"
    - "generating counterfactual explanations with natural language"
    - "explanation in artificial intelligence insights from the social sciences"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-2 (induced by source paper "towards a unified evaluation of explanation methods without ground truth"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "evaluating explainers via perturbation"
    - "can i trust the explainer verifying post hoc explanatory methods"
    - "bim towards quantitative evaluation of interpretability methods with ground truth"
    - "evaluating recurrent neural network explanations"
    - "evaluating explanation without ground truth in interpretable machine learning"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "towards robust interpretability with self explaining neural networks"

Connected through hyperedge HE-3 (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"
    - "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"
    - "examining cnn representations with respect to dataset bias"

Connected through hyperedge HE-4 (induced by source paper "explaining neural networks semantically and quantitatively"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "quantifying layerwise information discarding of neural networks"
    - "interpreting cnns via decision trees"
    - "unsupervised learning of neural networks to explain neural networks"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "learning to explain an information theoretic perspective on model interpretation"
    - "network dissection quantifying interpretability of deep visual representations"
    - "interpreting cnn knowledge via an explanatory graph"

Connected through hyperedge HE-5 (induced by source paper "unsupervised learning of neural networks to explain neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "interpreting cnns via decision trees"
    - "visual interpretability for deep learning a survey"
    - "network dissection quantifying interpretability of deep visual representations"
    - "explaining the unexplained a class enhanced attentive response clear approach to understanding deep neural networks"
    - "visual explanations for convolutional neural networks via input resampling"
    - "interpreting cnn knowledge via an explanatory graph"
    - "dynamic routing between capsules"

Connected through hyperedge HE-6 (induced by source paper "explanation methods in deep learning users values concerns and challenges"):
  with members:
    - "methods for interpreting and understanding deep neural networks"
    - "deep learning a critical appraisal"
    - "audio adversarial examples targeted attacks on speech to text"
    - "beyond word importance contextual decomposition to extract interactions from lstms"
    - "interpretable explorable approximations of black box models"
    - "detecting adversarial attacks on neural network policies with visual foresight"
    - "recurrent relational networks"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-7 (induced by source paper "variational saccading efficient inference for large resolution images"):
  with members:
    - "a new benchmark and progress toward improved weakly supervised learning"
    - "visual interpretability for deep learning a survey"
    - "group normalization"
    - "hierarchical attentive recurrent tracking"
    - "a simple neural network module for relational reasoning"
    - "starcraft ii a new challenge for reinforcement learning"
    - "dynamic routing between capsules"
    - "interpretable r cnn"

Connected through hyperedge HE-8 (induced by source paper "explaining alphago interpreting contextual effects in neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "unsupervised learning of neural networks to explain neural networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "explaining the unexplained a class enhanced attentive response clear approach to understanding deep neural networks"
    - "dynamic routing between capsules"
    - "a unified approach to interpreting model predictions"
    - "towards interpretable deep neural networks by leveraging adversarial examples"
    - "opening the black box of deep neural networks via information"

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)