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: "mask cnn localizing parts and selecting descriptors for fine grained image recognition"
  Abstract: Fine-grained image recognition is a challenging computer vision problem, due to the small inter-class variations caused by highly similar subordinate categories, and the large intra-class variations in poses, scales and rotations. In this paper, we propose a novel end-to-end Mask-CNN model without the fully connected layers for fine-grained recognition. Based on the part annotations of fine-grained images, the proposed model consists of a fully convolutional network to both locate the discriminative parts (e.g., head and torso), and more importantly generate object/part masks for selecting useful and meaningful convolutional descriptors. After that, a four-stream Mask-CNN model is built for aggregating the selected object- and part-level descriptors simultaneously. The proposed Mask-CNN model has the smallest number of parameters, lowest feature dimensionality and highest recognition accuracy when compared with state-of-the-arts fine-grained approaches.

Connected through hyperedge HE-1 (induced by source paper "automatic surface area and volume prediction on ellipsoidal ham using deep learning"):
  with members:
    - "less is more micro expression recognition from video using apex frame"
    - "detect and track efficient pose estimation in videos"
    - "mask r cnn"
    - "feature pyramid networks for object detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-2 (induced by source paper "fine grained age estimation in the wild with attention lstm networks"):
  with members:
    - "residual networks of residual networks multilevel residual networks"
    - "deep label distribution learning with label ambiguity"
    - "age group and gender estimation in the wild with deep ror architecture"
    - "video summarization with attention based encoder decoder networks"
    - "dager deep age gender and emotion recognition using convolutional neural network"
    - "pyramidal ror for image classification"
    - "apparent age estimation using ensemble of deep learning models"
    - "deep networks with stochastic depth"

Connected through hyperedge HE-3 (induced by source paper "weakly supervised local attention network for fine grained visual classification"):
  with members:
    - "facial landmarks detection by self iterative regression based landmarks attention network"
    - "adversarial complementary learning for weakly supervised object localization"
    - "towards faster training of global covariance pooling networks by iterative matrix square root normalization"
    - "top down neural attention by excitation backprop"
    - "spatial transformer networks"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-4 (induced by source paper "deep attentional structured representation learning for visual recognition"):
  with members:
    - "statistically motivated second order pooling"
    - "improved bilinear pooling with cnns"
    - "deep fishernet for object classification"
    - "compact bilinear pooling"
    - "harvesting discriminative meta objects with deep cnn features for scene classification"
    - "netvlad cnn architecture for weakly supervised place recognition"
    - "learning deep features for discriminative localization"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "one shot fine grained instance retrieval"):
  with members:
    - "faster r cnn features for instance search"
    - "deep image retrieval learning global representations for image search"
    - "localizing by describing attribute guided attention localization for fine grained recognition"
    - "a large scale car dataset for fine grained categorization and verification"
    - "subset feature learning for fine grained category classification"
    - "compact bilinear pooling"
    - "bilinear cnn models for fine grained visual recognition"
    - "spatial transformer networks"

Connected through hyperedge HE-6 (induced by source paper "noisy softmax improving the generalization ability of dcnn via postponing the early softmax saturation"):
  with members:
    - "ti pooling transformation invariant pooling for feature learning in convolutional neural networks"
    - "deep networks with stochastic depth"
    - "identity mappings in deep residual networks"
    - "noisy activation functions"
    - "densely connected convolutional networks"
    - "understanding and improving convolutional neural networks via concatenated rectified linear units"
    - "a lightened cnn for deep face representation"
    - "robust face recognition via multimodal deep face representation"

Connected through hyperedge HE-7 (induced by source paper "learning a discriminative filter bank within a cnn for fine grained recognition"):
  with members:
    - "fine grained image classification via combining vision and language"
    - "collaborative layer wise discriminative learning in deep neural networks"
    - "ssd single shot multibox detector"
    - "learning discriminative features via label consistent neural network"
    - "mining discriminative triplets of patches for fine grained classification"
    - "fine grained categorization and dataset bootstrapping using deep metric learning with humans in the loop"
    - "a large scale car dataset for fine grained categorization and verification"
    - "fine grained image classification by exploring bipartite graph labels"

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)