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: "object part attention model for fine grained image classification"
  Abstract: Fine-grained image classification is to recognize hundreds of subcategories belonging to the same basic-level category, such as 200 subcategories belonging to the bird, which is highly challenging due to  large  variance in the same subcategory and  small  variance among different subcategories. Existing methods generally first locate the objects or parts and then discriminate which subcategory the image belongs to. However, they mainly have  two limitations : 1) relying on object or part annotations which are heavily labor consuming; and 2) ignoring the spatial relationships between the object and its parts as well as among these parts, both of which are significantly helpful for finding discriminative parts. Therefore, this paper proposes the  object-part attention model (OPAM)  for weakly supervised fine-grained image classification and the main novelties are: 1)  object-part attention model  integrates two level attentions:  object-level attention  localizes objects of images, and  part-level attention  selects discriminative parts of object. Both are jointly employed to learn multi-view and multi-scale features to enhance their mutual promotion; and 2)  Object-part spatial constraint model  combines two spatial constraints:  object spatial constraint  ensures selected parts highly representative and  part spatial constraint  eliminates redundancy and enhances discrimination of selected parts. Both are jointly employed to exploit the subtle and local differences for distinguishing the subcategories. Importantly, neither object nor part annotations are used in our proposed approach, which avoids the heavy labor consumption of labeling. Compared with more than ten state-of-the-art methods on four widely-used datasets, our OPAM approach achieves the best performance.

Connected through hyperedge HE-1 (induced by source paper "pay attention to the activations a modular attention mechanism for fine grained image recognition"):
  with members:
    - "multi attention multi class constraint for fine grained image recognition"
    - "learning to navigate for fine grained classification"
    - "learning to zoom a saliency based sampling layer for neural networks"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "attend and rectify a gated attention mechanism for fine grained recovery"
    - "learn to pay attention"
    - "attention is all you need"
    - "deep layer aggregation"

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"
    - "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 "learning to transfer learn"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "neural voice cloning with a few samples"
    - "domain adaptive transfer learning with specialist models"
    - "spottune transfer learning through adaptive fine tuning"
    - "autoaugment learning augmentation policies from data"
    - "exploring the limits of weakly supervised pretraining"
    - "bert pre training of deep bidirectional transformers for language understanding"

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"
    - "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"
    - "end to end comparative attention networks for person re identification"

Connected through hyperedge HE-5 (induced by source paper "what i see is what you see joint attention learning for first and third person video co analysis"):
  with members:
    - "hierarchical recurrent neural network for video summarization"
    - "boosted attention leveraging human attention for image captioning"
    - "interaction aware spatio temporal pyramid attention networks for action classification"
    - "semantic aware attention based deep object co segmentation"
    - "factual or emotional stylized image captioning with adaptive learning and attention"
    - "cross view image synthesis using conditional gans"
    - "harmonious attention network for person re identification"
    - "global pose estimation with an attention based recurrent network"

Connected through hyperedge HE-6 (induced by source paper "mining objects fully unsupervised object discovery and localization from a single image"):
  with members:
    - "attention based dropout layer for weakly supervised object localization"
    - "salient object detection in the deep learning era an in depth survey"
    - "salient object detection a survey"
    - "self produced guidance for weakly supervised object localization"
    - "adversarial complementary learning for weakly supervised object localization"
    - "fine grained discriminative localization via saliency guided faster r cnn"
    - "diversified visual attention networks for fine grained object classification"
    - "deep descriptor transforming for image co localization"

Connected through hyperedge HE-7 (induced by source paper "relational knowledge distillation"):
  with members:
    - "deep metric learning with bier boosting independent embeddings robustly"
    - "model compression via distillation and quantization"
    - "learning to navigate for fine grained classification"
    - "attention based ensemble for deep metric learning"
    - "label refinery improving imagenet classification through label progression"
    - "sampling matters in deep embedding learning"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "apprentice using knowledge distillation techniques to improve low precision network accuracy"

Connected through hyperedge HE-8 (induced by source paper "competing ratio loss for discriminative multi class image classification"):
  with members:
    - "residual networks of residual networks multilevel residual networks"
    - "taming the cross entropy loss"
    - "convolutional neural networks with alternately updated clique"
    - "negative log likelihood ratio loss for deep neural network classification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "focal loss for dense object detection"
    - "age group and gender estimation in the wild with deep ror architecture"
    - "dual path 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)