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: "part detector discovery in deep convolutional neural networks"
  Abstract: Current fine-grained classification approaches often rely on a robust localization of object parts to extract localized feature representations suitable for discrimination. However, part localization is a challenging task due to the large variation of appearance and pose. In this paper, we show how pre-trained convolutional neural networks can be used for robust and efficient object part discovery and localization without the necessity to actually train the network on the current dataset. Our approach called "part detector discovery" (PDD) is based on analyzing the gradient maps of the network outputs and finding activation centers spatially related to annotated semantic parts or bounding boxes. #R##N#This allows us not just to obtain excellent performance on the CUB200-2011 dataset, but in contrast to previous approaches also to perform detection and bird classification jointly without requiring a given bounding box annotation during testing and ground-truth parts during training. The code is available at this http URL and this https URL

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 "unsupervised part mining for fine grained image classification"):
  with members:
    - "object part attention model for fine grained image classification"
    - "multi attention multi class constraint for fine grained image recognition"
    - "fine grained discriminative localization via saliency guided faster r cnn"
    - "diversified visual attention networks for fine grained object classification"
    - "fully convolutional attention localization networks efficient attention localization for fine grained recognition"
    - "weakly supervised fine grained categorization with part based image representation"
    - "fully convolutional attention networks for fine grained recognition"
    - "mining discriminative triplets of patches for fine grained classification"

Connected through hyperedge HE-3 (induced by source paper "harnessing intrinsic noise in memristor hopfield neural networks for combinatorial optimization"):
  with members:
    - "puma a programmable ultra efficient memristor based accelerator for machine learning inference"
    - "mixed precision in memory computing"
    - "multiscale co design analysis of energy latency area and accuracy of a reram analog neural training accelerator"
    - "fault tolerant dot product engines"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"

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"
    - "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-5 (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"
    - "growing interpretable part graphs on convnets via multi shot learning"

Connected through hyperedge HE-6 (induced by source paper "interpreting cnns via decision trees"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "visual interpretability for deep learning a survey"
    - "unsupervised learning of neural networks to explain neural networks"
    - "do explanations make vqa models more predictable to a human"
    - "network transplanting"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "network dissection quantifying interpretability of deep visual representations"
    - "interpreting cnn knowledge via an explanatory graph"

Connected through hyperedge HE-7 (induced by source paper "learning semantic part based models from google images"):
  with members:
    - "learning the semantic structure of objects from web supervision"
    - "webly supervised learning of convolutional networks"
    - "hypercolumns for object segmentation and fine grained localization"
    - "part based r cnns for fine grained category detection"
    - "semantic part segmentation using compositional model combining shape and appearance"
    - "detect what you can detecting and representing objects using holistic models and body parts"
    - "actions and attributes from wholes and parts"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "spot that bird a location based bird game"):
  with members:
    - "fine grained categorization and dataset bootstrapping using deep metric learning with humans in the loop"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"

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)