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: "neural activation constellations unsupervised part model discovery with convolutional networks"
  Abstract: Part models of object categories are essential for challenging recognition tasks, where differences in categories are subtle and only reflected in appearances of small parts of the object. We present an approach that is able to learn part models in a completely unsupervised manner, without part annotations and even without given bounding boxes during learning. The key idea is to find constellations of neural activation patterns computed using convolutional neural networks. In our experiments, we outperform existing approaches for fine-grained recognition on the CUB200-2011, NA birds, Oxford PETS, and Oxford Flowers dataset in case no part or bounding box annotations are available and achieve state-of-the-art performance for the Stanford Dog dataset. We also show the benefits of neural constellation models as a data augmentation technique for fine-tuning. Furthermore, our paper unites the areas of generic and fine-grained classification, since our approach is suitable for both scenarios. The source code of our method is available online at this http 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 "fine grained visual textual representation learning"):
  with members:
    - "one shot fine grained instance retrieval"
    - "fine grained image classification via combining vision and language"
    - "low rank bilinear pooling for fine grained classification"
    - "learning deep representations of fine grained visual descriptions"
    - "generative adversarial text to image synthesis"
    - "weakly supervised fine grained categorization with part based image representation"
    - "hierarchical question image co attention for visual question answering"
    - "fine grained categorization and dataset bootstrapping using deep metric learning with humans in the loop"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised complementary parts models for fine grained image classification from the bottom up"):
  with members:
    - "large scale fine grained categorization and domain specific transfer learning"
    - "multi attention multi class constraint for fine grained image recognition"
    - "features for multi target multi camera tracking and re identification"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "weakly supervised semantic segmentation by iteratively mining common object features"
    - "hierarchical bilinear pooling for fine grained visual recognition"
    - "explicit inductive bias for transfer learning with convolutional networks"
    - "network dissection quantifying interpretability of deep visual representations"

Connected through hyperedge HE-4 (induced by source paper "saliency for fine grained object recognition in domains with scarce training data"):
  with members:
    - "large scale fine grained categorization and domain specific transfer learning"
    - "deep visual attention prediction"
    - "feature space transfer for data augmentation"
    - "dynamic few shot visual learning without forgetting"
    - "deep layer aggregation"
    - "prototypical networks for few shot learning"
    - "exploiting inter image similarity and ensemble of extreme learners for fixation prediction using deep features"
    - "fine grained categorization via cnn based automatic extraction and integration of object level and part level features"

Connected through hyperedge HE-5 (induced by source paper "knowledge consistency between neural networks and beyond"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "similarity of neural network representations revisited"
    - "quantifying layerwise information discarding of neural networks"
    - "towards a unified evaluation of explanation methods without ground truth"
    - "utility analysis of network architectures for 3d point cloud processing"
    - "stiffness a new perspective on generalization in neural networks"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "towards understanding learning representations to what extent do different neural networks learn the same representation"

Connected through hyperedge HE-6 (induced by source paper "unsupervised semantic based aggregation of deep convolutional features"):
  with members:
    - "a discriminatively learned cnn embedding for person reidentification"
    - "unsupervised part based weighting aggregation of deep convolutional features for image retrieval"
    - "iterative manifold embedding layer learned by incomplete data for large scale image retrieval"
    - "fine tuning cnn image retrieval with no human annotation"
    - "residual attention network for image classification"
    - "mask r cnn"
    - "selective convolutional descriptor aggregation for fine grained image retrieval"
    - "end to end learning of deep visual representations for image retrieval"

Connected through hyperedge HE-7 (induced by source paper "see better before looking closer weakly supervised data augmentation network for fine grained visual classification"):
  with members:
    - "multi attention multi class constraint for fine grained image recognition"
    - "facial landmarks detection by self iterative regression based landmarks attention network"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "learning to navigate for fine grained classification"
    - "adversarial complementary learning for weakly supervised object localization"
    - "autoaugment learning augmentation policies from data"
    - "improved bilinear pooling with cnns"
    - "pairwise confusion for fine grained visual classification"

Connected through hyperedge HE-8 (induced by source paper "l relu piece wise linear activation functions for deep fine grained visual categorization"):
  with members:
    - "efficient and accurate estimation of lipschitz constants for deep neural networks"
    - "universal lipschitz approximation in bounded depth neural networks"
    - "multi attention multi class constraint for fine grained image recognition"
    - "sorting out lipschitz function approximation"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "l2 nonexpansive neural networks"
    - "fine grained representation learning and recognition by exploiting hierarchical semantic embedding"
    - "self normalizing neural 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)