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: "on the reconstruction of face images from deep face templates"
  Abstract: State-of-the-art face recognition systems are based on deep (convolutional) neural networks. Therefore, it is imperative to determine to what extent face templates derived from deep networks can be inverted to obtain the original face image. In this paper, we study the vulnerabilities of a state-of-the-art face recognition system based on template reconstruction attack. We propose a neighborly de-convolutional neural network ( NbNet ) to reconstruct face images from their deep templates. In our experiments, we assumed that no knowledge about the target subject and the deep network are available. To train the  NbNet  reconstruction models, we augmented two benchmark face datasets (VGG-Face and Multi-PIE) with a large collection of images synthesized using a face generator. The proposed reconstruction was evaluated using type-I (comparing the reconstructed images against the original face images used to generate the deep template) and type-II (comparing the reconstructed images against a different face image of the same subject) attacks. Given the images reconstructed from  NbNets , we show that for verification, we achieve TAR of 95.20 percent (58.05 percent) on LFW under type-I (type-II) attacks @ FAR of 0.1 percent. Besides, 96.58 percent (92.84 percent) of the images reconstructed from templates of partition  fa  ( fb ) can be identified from partition  fa  in color FERET. Our study demonstrates the need to secure deep templates in face recognition systems.

Connected through hyperedge HE-1 (induced by source paper "a novel classification selection approach for the self updating of template based face recognition systems"):
  with members:
    - "vggface2 a dataset for recognising faces across pose and age"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "squeeze and excitation networks"
    - "ms celeb 1m a dataset and benchmark for large scale face recognition"
    - "deep residual learning for image recognition"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-2 (induced by source paper "scalable facial image compression with deep feature reconstruction"):
  with members:
    - "vggface2 a dataset for recognising faces across pose and age"
    - "end to end optimized image compression"
    - "facenet a unified embedding for face recognition and clustering"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "reconstruction of privacy sensitive data from protected templates"):
  with members:
    - "learning to compress and search visual data in large scale systems"
    - "privacy preserving identification via layered sparse code design distributed servers and multiple access authorization"
    - "sparse ternary codes for similarity search have higher coding gain than dense binary codes"
    - "privacy preserving identification using sparse approximation with ambiguization"
    - "inverting visual representations with convolutional networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "a genetic algorithm enabled similarity based attack on cancellable biometrics"):
  with members:
    - "arcface additive angular margin loss for deep face recognition"
    - "ranking based locality sensitive hashing enabled cancelable biometrics index of max hashing"

Connected through hyperedge HE-5 (induced by source paper "secure face matching using fully homomorphic encryption"):
  with members:
    - "sphereface deep hypersphere embedding for face recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "surpassing human level face verification performance on lfw with gaussianface"
    - "learning face representation from scratch"
    - "deep learning face attributes in the wild"
    - "experiments with random projection"
    - "secure iris authentication using visual cryptography"

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)