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: "a survey on heterogeneous face recognition sketch infra red 3d and low resolution"
  Abstract: Heterogeneous face recognition (HFR) refers to matching face imagery across different domains. It has received much interest from the research community as a result of its profound implications in law enforcement. A wide variety of new invariant features, cross-modality matching models and heterogeneous datasets being established in recent years. This survey provides a comprehensive review of established techniques and recent developments in HFR. Moreover, we offer a detailed account of datasets and benchmarks commonly used for evaluation. We finish by assessing the state of the field and discussing promising directions for future research.

Connected through hyperedge HE-1 (induced by source paper "attribute guided deep polarimetric thermal to visible face recognition"):
  with members:
    - "toward marker free 3d pose estimation in lifting a deep multi view solution"
    - "generative adversarial network based synthesis of visible faces from polarimetric thermal faces"
    - "perceptual losses for real time style transfer and super resolution"
    - "face attribute prediction using off the shelf cnn features"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "multi task cnn model for attribute prediction"
    - "deep perceptual mapping for thermal to visible face recognition"

Connected through hyperedge HE-2 (induced by source paper "deep cross polarimetric thermal to visible face recognition"):
  with members:
    - "learning cross modal deep representations for robust pedestrian detection"
    - "unified deep supervised domain adaptation and generalization"
    - "deepid3 face recognition with very deep neural networks"
    - "facenet a unified embedding for face recognition and clustering"
    - "deep perceptual mapping for thermal to visible face recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "shared representation learning for heterogeneous face recognition"
    - "deeply learned face representations are sparse selective and robust"

Connected through hyperedge HE-3 (induced by source paper "frankenstein learning deep face representations using small data"):
  with members:
    - "face alignment in full pose range a 3d total solution"
    - "do we really need to collect millions of faces for effective face recognition"
    - "mocap guided data augmentation for 3d pose estimation in the wild"
    - "multi directional multi level dual cross patterns for robust face recognition"
    - "targeting ultimate accuracy face recognition via deep embedding"
    - "megaface a million faces for recognition at scale"
    - "semantic pose using deep networks trained on synthetic rgb d"
    - "deepid3 face recognition with very deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "a comprehensive survey on pose invariant face recognition"):
  with members:
    - "multi view intact space learning"
    - "multi directional multi level dual cross patterns for robust face recognition"
    - "robust face recognition via multimodal deep face representation"
    - "facenet a unified embedding for face recognition and clustering"
    - "facial feature point detection a comprehensive survey"
    - "effective face frontalization in unconstrained images"
    - "recover canonical view faces in the wild with deep neural networks"
    - "deeply coupled auto encoder networks for cross view 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)