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 stacked cnn for fine grained visual categorization"
  Abstract: In the context of fine-grained visual categorization, the ability to interpret models as human-understandable visual manuals is sometimes as important as achieving high classification accuracy. In this paper, we propose a novel Part-Stacked CNN architecture that explicitly explains the fine-grained recognition process by modeling subtle differences from object parts. Based on manually-labeled strong part annotations, the proposed architecture consists of a fully convolutional network to locate multiple object parts and a two-stream classification network that en- codes object-level and part-level cues simultaneously. By adopting a set of sharing strategies between the computation of multiple object parts, the proposed architecture is very efficient running at 20 frames/sec during inference. Experimental results on the CUB-200-2011 dataset reveal the effectiveness of the proposed architecture, from both the perspective of classification accuracy and model interpretability.

Connected through hyperedge HE-1 (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-2 (induced by source paper "fine grained visual classification via progressive multi granularity training of jigsaw patches"):
  with members:
    - "the devil is in the channels mutual channel loss for fine grained image classification"
    - "singan learning a generative model from a single natural image"
    - "cross x learning for fine grained visual categorization"
    - "weakly supervised complementary parts models for fine grained image classification from the bottom up"
    - "learning to navigate for fine grained classification"
    - "iterative reorganization with weak spatial constraints solving arbitrary jigsaw puzzles for unsupervised representation learning"
    - "a fully progressive approach to single image super resolution"
    - "a style based generator architecture for generative adversarial networks"

Connected through hyperedge HE-3 (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-4 (induced by source paper "attribute aware attention model for fine grained representation learning"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "object part attention model for fine grained image classification"
    - "person re identification by camera correlation aware feature augmentation"
    - "scalable person re identification on supervised smoothed manifold"
    - "a discriminatively learned cnn embedding for person reidentification"
    - "re ranking person re identification with k reciprocal encoding"
    - "person re identification by deep joint learning of multi loss classification"
    - "autoencoder inspired unsupervised feature selection"

Connected through hyperedge HE-5 (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"

Connected through hyperedge HE-6 (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-7 (induced by source paper "interpretable image recognition with hierarchical prototypes"):
  with members:
    - "tree cnn a deep convolutional neural network for lifelong learning"
    - "this looks like that deep learning for interpretable image recognition"
    - "branchconnect image categorization with learned branch connections"
    - "does your model know the digit 6 is not a cat a less biased evaluation of outlier detectors"
    - "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem"
    - "deep learning for case based reasoning through prototypes a neural network that explains its predictions"
    - "network dissection quantifying interpretability of deep visual representations"
    - "axiomatic attribution for deep networks"

Connected through hyperedge HE-8 (induced by source paper "fine grained visual recognition with batch confusion norm"):
  with members:
    - "looking for the devil in the details learning trilinear attention sampling network for fine grained image recognition"
    - "weakly supervised complementary parts models for fine grained image classification from the bottom up"
    - "object part attention model for fine grained image classification"
    - "learning to navigate for fine grained classification"
    - "free form image inpainting with gated convolution"
    - "hierarchical bilinear pooling for fine grained visual recognition"
    - "improved bilinear pooling with cnns"
    - "pairwise confusion for fine grained visual classification"

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)