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: "learning a discriminative filter bank within a cnn for fine grained recognition"
  Abstract: Compared to earlier multistage frameworks using CNN features, recent end-to-end deep approaches for fine-grained recognition essentially enhance the mid-level learning capability of CNNs. Previous approaches achieve this by introducing an auxiliary network to infuse localization information into the main classification network, or a sophisticated feature encoding method to capture higher order feature statistics. We show that mid-level representation learning can be enhanced within the CNN framework, by learning a bank of convolutional filters that capture class-specific discriminative patches without extra part or bounding box annotations. Such a filter bank is well structured, properly initialized and discriminatively learned through a novel asymmetric multi-stream architecture with convolutional filter supervision and a non-random layer initialization. Experimental results show that our approach achieves state-of-the-art on three publicly available fine-grained recognition datasets (CUB-200-2011, Stanford Cars and FGVC-Aircraft). Ablation studies and visualizations are provided to understand our approach.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised attention pyramid convolutional neural network for fine grained visual classification"):
  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"
    - "dropblock a regularization method for convolutional networks"
    - "learning to navigate for fine grained classification"
    - "hierarchical bilinear pooling for fine grained visual recognition"
    - "low rank bilinear pooling for fine grained classification"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-2 (induced by source paper "the devil is in the channels mutual channel loss for fine grained image classification"):
  with members:
    - "deep learning for free hand sketch a survey"
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "multi graph transformer for free hand sketch recognition"
    - "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"
    - "cosface large margin cosine loss for deep face recognition"
    - "learning to navigate for fine grained classification"

Connected through hyperedge HE-3 (induced by source paper "layer wise pruning and auto tuning of layer wise learning rates in fine tuning of deep networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "backbone can not be trained at once rolling back to pre trained network for person re identification"
    - "multi similarity loss with general pair weighting for deep metric learning"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "do better imagenet models transfer better"
    - "classification is a strong baseline for deep metric learning"
    - "learning to navigate for fine grained classification"
    - "deep randomized ensembles for metric learning"

Connected through hyperedge HE-4 (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-5 (induced by source paper "spatial scale aligned network for fine grained recognition"):
  with members:
    - "looking for the devil in the details learning trilinear attention sampling network for fine grained image recognition"
    - "maximum entropy fine grained classification"
    - "learning to navigate for fine grained classification"
    - "towards faster training of global covariance pooling networks by iterative matrix square root normalization"
    - "feature pyramid networks for object detection"
    - "discriminative scale space tracking"
    - "compact bilinear pooling"
    - "bilinear cnn models for fine grained visual recognition"

Connected through hyperedge HE-6 (induced by source paper "attention convolutional binary neural tree for fine grained visual categorization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "see better before looking closer weakly supervised data augmentation network for fine grained visual classification"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "looking for the devil in the details learning trilinear attention sampling network for fine grained image recognition"
    - "knowledge embedded representation learning for fine grained image recognition"
    - "fine grained classification of cervical cells using morphological and appearance based convolutional neural networks"
    - "maximum entropy fine grained classification"
    - "adaptive neural trees"

Connected through hyperedge HE-7 (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-8 (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"

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)