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: "looking for the devil in the details learning trilinear attention sampling network for fine grained image recognition"
  Abstract: Learning subtle yet discriminative features (e.g., beak and eyes for a bird) plays a significant role in fine-grained image recognition. Existing attention-based approaches localize and amplify significant parts to learn fine-grained details, which often suffer from a limited number of parts and heavy computational cost. In this paper, we propose to learn such fine-grained features from hundreds of part proposals by Trilinear Attention Sampling Network (TASN) in an efficient teacher-student manner. Specifically, TASN consists of 1) a trilinear attention module, which generates attention maps by modeling the inter-channel relationships, 2) an attention-based sampler which highlights attended parts with high resolution, and 3) a feature distiller, which distills part features into a global one by weight sharing and feature preserving strategies. Extensive experiments verify that TASN yields the best performance under the same settings with the most competitive approaches, in iNaturalist-2017, CUB-Bird, and Stanford-Cars datasets.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised attention pyramid convolutional neural network for fine grained visual classification"):
  with members:
    - "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"
    - "hypernet towards accurate region proposal generation and joint object detection"

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"
    - "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"
    - "rethinking feature distribution for loss functions in image classification"

Connected through hyperedge HE-3 (induced by source paper "channel interaction networks for fine grained image categorization"):
  with members:
    - "multi attention multi class constraint for fine grained image recognition"
    - "learning to navigate for fine grained classification"
    - "compact generalized non local network"
    - "hierarchical bilinear pooling for fine grained visual recognition"
    - "fine grained representation learning and recognition by exploiting hierarchical semantic embedding"
    - "non local neural networks"
    - "attention is all you need"
    - "beyond triplet loss a deep quadruplet network for person re identification"

Connected through hyperedge HE-4 (induced by source paper "spatial scale aligned network for fine grained recognition"):
  with members:
    - "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"
    - "learning a discriminative filter bank within a cnn for fine grained recognition"
    - "discriminative scale space tracking"
    - "compact bilinear pooling"
    - "bilinear cnn models for fine grained visual recognition"

Connected through hyperedge HE-5 (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"
    - "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"
    - "learn to pay attention"

Connected through hyperedge HE-6 (induced by source paper "fine grained recognition accounting for subtle differences between similar classes"):
  with members:
    - "weakly supervised complementary parts models for fine grained image classification from the bottom up"
    - "multi attention multi class constraint for fine grained image recognition"
    - "deep texture manifold for ground terrain recognition"
    - "maximum entropy fine grained classification"
    - "learning to navigate for fine grained classification"
    - "rethinking feature distribution for loss functions in image classification"
    - "pairwise confusion for fine grained visual classification"
    - "towards faster training of global covariance pooling networks by iterative matrix square root normalization"

Connected through hyperedge HE-7 (induced by source paper "fine grained visual recognition with batch confusion norm"):
  with members:
    - "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"
    - "low rank bilinear pooling for fine grained classification"

Connected through hyperedge HE-8 (induced by source paper "patchy image structure classification using multi orientation region transform"):
  with members:
    - "probabilistic plant modeling via multi view image to image translation"
    - "learning to navigate for fine grained classification"
    - "improved bilinear pooling with cnns"
    - "towards faster training of global covariance pooling networks by iterative matrix square root normalization"
    - "deep residual learning for image recognition"
    - "part stacked cnn for fine grained visual categorization"
    - "very deep convolutional networks for large scale image recognition"
    - "decaf a deep convolutional activation feature for generic visual recognition"

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)