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: "image set based collaborative representation for face recognition"
  Abstract: With the rapid development of digital imaging and communication technologies, image set based face recognition (ISFR) is becoming increasingly important. One key issue of ISFR is how to effectively and efficiently represent the query face image set by using the gallery face image sets. The set-to-set distance based methods ignore the relationship between gallery sets, while representing the query set images individually over the gallery sets ignores the correlation between query set images. In this paper, we propose a novel image set based collaborative representation and classification method for ISFR. By modeling the query set as a convex or regularized hull, we represent this hull collaboratively over all the gallery sets. With the resolved representation coefficients, the distance between the query set and each gallery set can then be calculated for classification. The proposed model naturally and effectively extends the image based collaborative representation to an image set based one, and our extensive experiments on benchmark ISFR databases show the superiority of the proposed method to state-of-the-art ISFR methods under different set sizes in terms of both recognition rate and efficiency.

Connected through hyperedge HE-1 (induced by source paper "trunk branch ensemble convolutional neural networks for video based face recognition"):
  with members:
    - "multi directional multi level dual cross patterns for robust face recognition"
    - "fine grained pose prediction normalization and recognition"
    - "targeting ultimate accuracy face recognition via deep embedding"
    - "robust face recognition via multimodal deep face representation"
    - "deepid3 face recognition with very deep neural networks"
    - "bilinear cnn models for fine grained visual recognition"
    - "spatial transformer networks"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-2 (induced by source paper "nonnegative binary matrix factorization with a d wave quantum annealer"):
  with members:
    - "fast clique minor generation in chimera qubit connectivity graphs"

Connected through hyperedge HE-3 (induced by source paper "large margin learning in set to set similarity comparison for person re identification"):
  with members:
    - "cross domain visual matching via generalized similarity measure and feature learning"
    - "learning a discriminative null space for person re identification"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "deep feature learning with relative distance comparison for person re identification"
    - "deep residual learning for image recognition"
    - "bit scalable deep hashing with regularized similarity learning for image retrieval and person re identification"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "person re identification by local maximal occurrence representation and metric learning"

Connected through hyperedge HE-4 (induced by source paper "modal regression based atomic representation for robust face recognition"):
  with members:
    - "regularized robust coding for face recognition"

Connected through hyperedge HE-5 (induced by source paper "joint representation classification for collective face recognition"):
  with members:
    - "complexity of unconstrained l_2 l_p minimization"

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)