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: "deep learning multi view representation for face recognition"
  Abstract: Various factors, such as identities, views (poses), and illuminations, are coupled in face images. Disentangling the identity and view representations is a major challenge in face recognition. Existing face recognition systems either use handcrafted features or learn features discriminatively to improve recognition accuracy. This is different from the behavior of human brain. Intriguingly, even without accessing 3D data, human not only can recognize face identity, but can also imagine face images of a person under different viewpoints given a single 2D image, making face perception in the brain robust to view changes. In this sense, human brain has learned and encoded 3D face models from 2D images. To take into account this instinct, this paper proposes a novel deep neural net, named multi-view perceptron (MVP), which can untangle the identity and view features, and infer a full spectrum of multi-view images in the meanwhile, given a single 2D face image. The identity features of MVP achieve superior performance on the MultiPIE dataset. MVP is also capable to interpolate and predict images under viewpoints that are unobserved in the training data.

Connected through hyperedge HE-1 (induced by source paper "a study on various state of the art of the art face recognition system using deep learning techniques"):
  with members:
    - "sphereface deep hypersphere embedding for face recognition"
    - "a comprehensive analysis of deep learning based representation for face recognition"
    - "do deep neural networks learn facial action units when doing expression recognition"
    - "unconstrained face verification using deep cnn features"
    - "pcanet a simple deep learning baseline for image classification"
    - "building high level features using large scale unsupervised learning"

Connected through hyperedge HE-2 (induced by source paper "guiding infogan with semi supervision"):
  with members:
    - "disentangling factors of variation in deep representations using adversarial training"
    - "improving variational inference with inverse autoregressive flow"
    - "amortised map inference for image super resolution"
    - "unrolled generative adversarial networks"
    - "improved techniques for training gans"
    - "wide residual networks"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "importance weighted autoencoders"

Connected through hyperedge HE-3 (induced by source paper "learning deep representation for face alignment with auxiliary attributes"):
  with members:
    - "surpassing human level face verification performance on lfw with gaussianface"
    - "recover canonical view faces in the wild with deep neural networks"
    - "deep learning face representation by joint identification verification"
    - "deep regression for face alignment"
    - "heterogeneous multi task learning for human pose estimation with deep convolutional neural network"
    - "a convex formulation for learning task relationships in multi task learning"

Connected through hyperedge HE-4 (induced by source paper "multimodal transfer deep learning with applications in audio visual recognition"):
  with members:
    - "a survey time travel in deep learning space an introduction to deep learning models and how deep learning models evolved from the initial ideas"
    - "mining associated text and images with dual wing harmoniums"

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)