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: "growing interpretable part graphs on convnets via multi shot learning"
  Abstract: This paper proposes a learning strategy that extracts object-part concepts from a pre-trained convolutional neural network (CNN), in an attempt to 1) explore explicit semantics hidden in CNN units and 2) gradually grow a semantically interpretable graphical model on the pre-trained CNN for hierarchical object understanding. Given part annotations on very few (e.g., 3-12) objects, our method mines certain latent patterns from the pre-trained CNN and associates them with different semantic parts. We use a four-layer And-Or graph to organize the mined latent patterns, so as to clarify their internal semantic hierarchy. Our method is guided by a small number of part annotations, and it achieves superior performance (about 13%-107% improvement) in part center prediction on the PASCAL VOC and ImageNet datasets.

Connected through hyperedge HE-1 (induced by source paper "interpretable cnns for object classification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "one pixel attack for fooling deep neural networks"
    - "visual interpretability for deep learning a survey"
    - "explaining neural networks semantically and quantitatively"
    - "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"
    - "visual explanations for convolutional neural networks via input resampling"

Connected through hyperedge HE-2 (induced by source paper "saliency tubes visual explanations for spatio temporal convolutions"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "scaling egocentric vision the epic kitchens dataset"
    - "multi fiber networks for video recognition"
    - "methods for interpreting and understanding deep neural networks"
    - "analyzing human human interactions a survey"
    - "network dissection quantifying interpretability of deep visual representations"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "excitation backprop for rnns"

Connected through hyperedge HE-3 (induced by source paper "a game method for improving the interpretability of convolution neural network"):
  with members:
    - "lyrics a general interface layer to integrate ai and deep learning"
    - "interpreting cnns via decision trees"
    - "on a convex logic fragment for learning and reasoning"
    - "interpreting deep visual representations via network dissection"
    - "interpretable convolutional neural networks"
    - "learning deep features for discriminative localization"

Connected through hyperedge HE-4 (induced by source paper "explaining explanations an overview of interpretability of machine learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "multimodal explanations justifying decisions and pointing to the evidence"
    - "visual interpretability for deep learning a survey"
    - "unsupervised learning of neural networks to explain neural networks"
    - "a survey of methods for explaining black box models"
    - "smoothgrad removing noise by adding noise"
    - "network dissection quantifying interpretability of deep visual representations"
    - "adversarial examples are not easily detected bypassing ten detection methods"

Connected through hyperedge HE-5 (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"

Connected through hyperedge HE-6 (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"
    - "towards interpretable r cnn by unfolding latent structures"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "explanatory graphs for cnns"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "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"
    - "interpreting cnn knowledge via an explanatory graph"
    - "dynamic routing between capsules"
    - "examining cnn representations with respect to dataset bias"

Connected through hyperedge HE-8 (induced by source paper "mining interpretable aog representations from convolutional networks via active question answering"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "one pixel attack for fooling deep neural networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "examining cnn representations with respect to dataset bias"
    - "mining object parts from cnns via active question answering"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "harnessing deep neural networks with logic rules"
    - "identifying unknown unknowns in the open world representations and policies for guided exploration"

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)