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: "bird species categorization using pose normalized deep convolutional nets"
  Abstract: We propose an architecture for fine-grained visual categorization that approaches expert human performance in the classification of bird species. Our architecture first computes an estimate of the object's pose; this is used to compute local image features which are, in turn, used for classification. The features are computed by applying deep convolutional nets to image patches that are located and normalized by the pose. We perform an empirical study of a number of pose normalization schemes, including an investigation of higher order geometric warping functions. We propose a novel graph-based clustering algorithm for learning a compact pose normalization space. We perform a detailed investigation of state-of-the-art deep convolutional feature implementations and fine-tuning feature learning for fine-grained classification. We observe that a model that integrates lower-level feature layers with pose-normalized extraction routines and higher-level feature layers with unaligned image features works best. Our experiments advance state-of-the-art performance on bird species recognition, with a large improvement of correct classification rates over previous methods (75% vs. 55-65%).

Connected through hyperedge HE-1 (induced by source paper "fine grained visual textual representation learning"):
  with members:
    - "one shot fine grained instance retrieval"
    - "fine grained image classification via combining vision and language"
    - "low rank bilinear pooling for fine grained classification"
    - "learning deep representations of fine grained visual descriptions"
    - "generative adversarial text to image synthesis"
    - "weakly supervised fine grained categorization with part based image representation"
    - "hierarchical question image co attention for visual question answering"
    - "fine grained categorization and dataset bootstrapping using deep metric learning with humans in the loop"

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"
    - "learning to navigate for fine grained classification"
    - "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"

Connected through hyperedge HE-3 (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"
    - "learning to navigate for fine grained classification"

Connected through hyperedge HE-4 (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"
    - "looking for the devil in the details learning trilinear attention sampling network for fine grained image recognition"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "attribute aware attention model for fine grained representation learning"):
  with members:
    - "improving person re identification by attribute and identity learning"
    - "object part attention model for fine grained image classification"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "cross x learning for fine grained visual categorization"):
  with members:
    - "maximum entropy fine grained classification"
    - "learning to navigate for fine grained classification"
    - "sampling matters in deep embedding learning"
    - "deep layer aggregation"
    - "dynamic computational time for visual attention"
    - "fully convolutional attention localization networks efficient attention localization for fine grained recognition"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "ssd single shot multibox detector"

Connected through hyperedge HE-7 (induced by source paper "fast fine grained image classification via weakly supervised discriminative localization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "nearest neighbour radial basis function solvers for deep neural networks"
    - "fine grained discriminative localization via saliency guided faster r cnn"
    - "diversified visual attention networks for fine grained object classification"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"
    - "localizing by describing attribute guided attention localization for fine grained recognition"
    - "weakly supervised fine grained categorization with part based image representation"
    - "learning a discriminative filter bank within a cnn for fine grained recognition"

Connected through hyperedge HE-8 (induced by source paper "unsupervised part mining for fine grained image classification"):
  with members:
    - "object part attention model for fine grained image classification"
    - "multi attention multi class constraint for fine grained image recognition"
    - "fine grained discriminative localization via saliency guided faster r cnn"
    - "diversified visual attention networks for fine grained object classification"
    - "fully convolutional attention localization networks efficient attention localization for fine grained recognition"
    - "weakly supervised fine grained categorization with part based image representation"
    - "fully convolutional attention networks for fine grained recognition"
    - "mining discriminative triplets of patches for fine grained classification"

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)