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: "fine grained pose prediction normalization and recognition"
  Abstract: Pose variation and subtle differences in appearance are key challenges to fine-grained classification. While deep networks have markedly improved general recognition, many approaches to fine-grained recognition rely on anchoring networks to parts for better accuracy. Identifying parts to find correspondence discounts pose variation so that features can be tuned to appearance. To this end previous methods have examined how to find parts and extract pose-normalized features. These methods have generally separated fine-grained recognition into stages which first localize parts using hand-engineered and coarsely-localized proposal features, and then separately learn deep descriptors centered on inferred part positions. We unify these steps in an end-to-end trainable network supervised by keypoint locations and class labels that localizes parts by a fully convolutional network to focus the learning of feature representations for the fine-grained classification task. Experiments on the popular CUB200 dataset show that our method is state-of-the-art and suggest a continuing role for strong supervision.

Connected through hyperedge HE-1 (induced by source paper "hierarchical binary cnns for landmark localization with limited resources"):
  with members:
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "godp globally optimized dual pathway deep network architecture for facial landmark localization in the wild"
    - "binarized convolutional landmark localizers for human pose estimation and face alignment with limited resources"
    - "convolutional pose machines"
    - "stacked hourglass networks for human pose estimation"
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "deep neural networks are robust to weight binarization and other non linear distortions"
    - "aggregated residual transformations for deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "geo aware networks for fine grained recognition"):
  with members:
    - "large scale fine grained categorization and domain specific transfer learning"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "the devil is in the tails fine grained classification in the wild"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "fine grained recognition in the wild a multi task domain adaptation approach"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "cvae gan fine grained image generation through asymmetric training"
    - "film visual reasoning with a general conditioning layer"

Connected through hyperedge HE-3 (induced by source paper "lightweight real time makeup try on in mobile browsers with tiny cnn models for facial tracking"):
  with members:
    - "look at boundary a boundary aware face alignment algorithm"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "an occluded stacked hourglass approach to facial landmark localization and occlusion estimation"
    - "deep alignment network a convolutional neural network for robust face alignment"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "mask r cnn"
    - "stacked hourglass networks for human pose estimation"

Connected through hyperedge HE-4 (induced by source paper "large scale fine grained categorization and domain specific transfer learning"):
  with members:
    - "teaching categories to human learners with visual explanations"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "generalized orderless pooling performs implicit salient matching"
    - "the devil is in the tails fine grained classification in the wild"
    - "learning transferable architectures for scalable image recognition"
    - "deep layer aggregation"
    - "fine grained recognition in the wild a multi task domain adaptation approach"
    - "squeeze and excitation networks"

Connected through hyperedge HE-5 (induced by source paper "aligned to the object not to the image a unified pose aligned representation for fine grained recognition"):
  with members:
    - "large scale fine grained categorization and domain specific transfer learning"
    - "pooling is neither necessary nor sufficient for appropriate deformation stability in cnns"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "learning steerable filters for rotation equivariant cnns"
    - "rotation equivariant vector field networks"
    - "pairwise confusion for fine grained visual classification"
    - "diversified visual attention networks for fine grained object classification"
    - "low rank bilinear pooling for fine grained classification"

Connected through hyperedge HE-6 (induced by source paper "trunk branch ensemble convolutional neural networks for video based face recognition"):
  with members:
    - "multi directional multi level dual cross patterns for robust face recognition"
    - "targeting ultimate accuracy face recognition via deep embedding"
    - "robust face recognition via multimodal deep face representation"
    - "deepid3 face recognition with very deep neural networks"
    - "bilinear cnn models for fine grained visual recognition"
    - "spatial transformer networks"
    - "facenet a unified embedding for face recognition and clustering"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-7 (induced by source paper "part aware fine grained object categorization using weakly supervised part detection network"):
  with members:
    - "object part attention model for fine grained image classification"
    - "multiple instance detection network with online instance classifier refinement"
    - "diversified visual attention networks for fine grained object classification"
    - "dynamic computational time for visual attention"
    - "fully convolutional attention localization networks efficient attention localization for fine grained recognition"
    - "real time fine grained categorization with accuracy and interpretability"
    - "weakly supervised fine grained categorization with part based image representation"
    - "fully convolutional attention networks for fine grained recognition"

Connected through hyperedge HE-8 (induced by source paper "pairwise confusion for fine grained visual classification"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "generalized orderless pooling performs implicit salient matching"
    - "modeling image virality with pairwise spatial transformer networks"
    - "improved bilinear pooling with cnns"
    - "low rank bilinear pooling for fine grained classification"
    - "densely connected convolutional networks"
    - "deep learning the city quantifying urban perception at a global scale"
    - "weakly supervised fine grained categorization with part based image representation"

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)