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 to navigate for fine grained classification"
  Abstract: Fine-grained classification is challenging due to the difficulty of finding discriminative features. Finding those subtle traits that fully characterize the object is not straightforward. To handle this circumstance, we propose a novel self-supervision mechanism to effectively localize informative regions without the need of bounding-box/part annotations. Our model, termed NTS-Net for Navigator-Teacher-Scrutinizer Network, consists of a Navigator agent, a Teacher agent and a Scrutinizer agent. In consideration of intrinsic consistency between informativeness of the regions and their probability being ground-truth class, we design a novel training paradigm, which enables Navigator to detect most informative regions under the guidance from Teacher. After that, the Scrutinizer scrutinizes the proposed regions from Navigator and makes predictions. Our model can be viewed as a multi-agent cooperation, wherein agents benefit from each other, and make progress together. NTS-Net can be trained end-to-end, while provides accurate fine-grained classification predictions as well as highly informative regions during inference. We achieve state-of-the-art performance in extensive benchmark datasets.

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:
    - "object part attention model for fine grained image classification"
    - "multi attention multi class constraint for fine grained image recognition"
    - "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 "daf net a saliency based weakly supervised method of dual attention fusion for fine grained image classification"):
  with members:
    - "global second order pooling convolutional networks"
    - "yolov3 an incremental improvement"
    - "focal loss for dense object detection"
    - "improved bilinear pooling with cnns"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "diversified visual attention networks for fine grained object classification"
    - "low rank bilinear pooling for fine grained classification"
    - "selective convolutional descriptor aggregation for fine grained image retrieval"

Connected through hyperedge HE-3 (induced by source paper "fine grained image classification and retrieval by combining visual and locally pooled textual features"):
  with members:
    - "towards vqa models that can read"
    - "block bilinear superdiagonal fusion for visual question answering and visual relationship detection"
    - "on the variance of the adaptive learning rate and beyond"
    - "doodle to search practical zero shot sketch based image retrieval"
    - "lookahead optimizer k steps forward 1 step back"
    - "single shot scene text retrieval"
    - "an end to end textspotter with explicit alignment and attention"
    - "e2e mlt an unconstrained end to end method for multi language scene text"

Connected through hyperedge HE-4 (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"
    - "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-5 (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"
    - "rethinking feature distribution for loss functions in image classification"

Connected through hyperedge HE-6 (induced by source paper "channel interaction networks for fine grained image categorization"):
  with members:
    - "looking for the devil in the details learning trilinear attention sampling network for fine grained image recognition"
    - "multi attention multi class constraint for fine grained image recognition"
    - "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-7 (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"
    - "deep randomized ensembles for metric learning"
    - "spottune transfer learning through adaptive fine tuning"

Connected through hyperedge HE-8 (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"
    - "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"
    - "progressive growing of gans for improved quality stability and variation"

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)