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: "interactively transferring cnn patterns for part localization"
  Abstract: In the scenario of one/multi-shot learning, conventional end-to-end learning strategies without sufficient supervision are usually not powerful enough to learn correct patterns from noisy signals. Thus, given a CNN pre-trained for object classification, this paper proposes a method that first summarizes the knowledge hidden inside the CNN into a dictionary of latent activation patterns, and then builds a new model for part localization by manually assembling latent patterns related to the target part via human interactions. We use very few (e.g., three) annotations of a semantic object part to retrieve certain latent patterns from conv-layers to represent the target part. We then visualize these latent patterns and ask users to further remove incorrect patterns, in order to refine part representation. With the guidance of human interactions, our method exhibited superior performance of part localization in experiments.

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"
    - "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-2 (induced by source paper "a logic based framework leveraging neural networks for studying the evolution of neurological disorders"):
  with members:
    - "the seventh answer set programming competition design and results"
    - "the external interface for extending wasp"
    - "asp core 2 input language format"
    - "a comprehensive survey on graph neural networks"
    - "interpreting cnns via decision trees"
    - "constraint answer set programming without grounding"
    - "automated verification of neural networks advances challenges and perspectives"
    - "visual interpretability for deep learning a survey"

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"
    - "towards interpretable r cnn by unfolding latent structures"
    - "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 "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"
    - "interpreting cnn knowledge via an explanatory graph"
    - "dynamic routing between capsules"
    - "examining cnn representations with respect to dataset bias"
    - "mining object parts from cnns via active question answering"

Connected through hyperedge HE-5 (induced by source paper "interpreting cnn knowledge via an explanatory graph"):
  with members:
    - "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"
    - "growing interpretable part graphs on convnets via multi shot learning"
    - "inverting visual representations with convolutional networks"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "deep residual learning for image recognition"
    - "understanding deep features with computer generated imagery"

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)