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 recognition datasets for biodiversity analysis"
  Abstract: In the following paper, we present and discuss challenging applications for fine-grained visual classification (FGVC): biodiversity and species analysis. We not only give details about two challenging new datasets suitable for computer vision research with up to 675 highly similar classes, but also present first results with localized features using convolutional neural networks (CNN). We conclude with a list of challenging new research directions in the area of visual classification for biodiversity research.

Connected through hyperedge HE-1 (induced by source paper "generate non software bugs to fool classifiers"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "robust audio adversarial example for a physical attack"
    - "adversarial patch"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "adversarial attacks against automatic speech recognition systems via psychoacoustic hiding"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "adversarial audio synthesis"
    - "generating adversarial examples with adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "fast enhanced ct metal artifact reduction using data domain deep learning"):
  with members:
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "cnn based segmentation of medical imaging data"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "deep learning for photoacoustic tomography from sparse data"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep convolutional neural network for inverse problems in imaging"

Connected through hyperedge HE-3 (induced by source paper "coconet a collaborative convolutional network"):
  with members:
    - "loop descriptor local optimal oriented pattern"
    - "the devil is in the tails fine grained classification in the wild"
    - "zero shot fine grained classification by deep feature learning with semantics"
    - "improved bilinear pooling with cnns"
    - "bilinear cnn models for fine grained visual recognition"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "distance metric learned collaborative representation classifier"):
  with members:
    - "coconet a collaborative convolutional network"
    - "object part attention model for fine grained image classification"
    - "a tutorial on distance metric learning mathematical foundations algorithms and software"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "pprocrc probabilistic collaboration of image patches"):
  with members:
    - "the devil is in the tails fine grained classification in the wild"
    - "loop descriptor encoding repeated local patterns for fine grained visual identification of lepidoptera"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "past present and future approaches using computer vision for animal re identification from camera trap data"):
  with members:
    - "face recognition primates in the wild"
    - "deep learning object detection methods for ecological camera trap data"
    - "dynamic routing between capsules"
    - "automatically identifying wild animals in camera trap images with deep learning"
    - "ssd single shot multibox detector"
    - "fine grained recognition in the noisy wild sensitivity analysis of convolutional neural networks approaches"
    - "automated visual fin identification of individual great white sharks"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-7 (induced by source paper "selective convolutional descriptor aggregation for fine grained image retrieval"):
  with members:
    - "good practice in cnn feature transfer"
    - "weakly supervised fine grained categorization with part based image representation"
    - "instance aware hashing for multi label image retrieval"
    - "bilinear cnn models for fine grained visual recognition"
    - "spatial transformer networks"
    - "cross dimensional weighting for aggregated deep convolutional features"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "weakly supervised fine grained categorization with part based image representation"):
  with members:
    - "spatial transformer networks"
    - "neural activation constellations unsupervised part model discovery with convolutional networks"
    - "fast r cnn"
    - "r cnn minus r"
    - "a comparison of dense region detectors for image search and fine grained classification"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"

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)