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: "face sketch matching via coupled deep transform learning"
  Abstract: Face sketch to digital image matching is an important challenge of face recognition that involves matching across different domains. Current research efforts have primarily focused on extracting domain invariant representations or learning a mapping from one domain to the other. In this research, we propose a novel transform learning based approach termed as DeepTransformer, which learns a transformation and mapping function between the features of two domains. The proposed formulation is independent of the input information and can be applied with any existing learned or hand-crafted feature. Since the mapping function is directional in nature, we propose two variants of DeepTransformer: (i) semi-coupled and (ii) symmetrically-coupled deep transform learning. This research also uses a novel IIIT-D Composite Sketch with Age (CSA) variations database which contains sketch images of 150 subjects along with age-separated digital photos. The performance of the proposed models is evaluated on a novel application of sketch-to-sketch matching, along with sketch-to-digital photo matching. Experimental results demonstrate the robustness of the proposed models in comparison to existing state-of-the-art sketch matching algorithms and a commercial face recognition system.

Connected through hyperedge HE-1 (induced by source paper "learning structure and strength of cnn filters for small sample size training"):
  with members:
    - "scaling the scattering transform deep hybrid networks"
    - "densely connected convolutional networks"
    - "matching networks for one shot learning"
    - "low shot visual recognition by shrinking and hallucinating features"
    - "a lightened cnn for deep face representation"
    - "all you need is a good init"
    - "reducing overfitting in deep networks by decorrelating representations"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-2 (induced by source paper "attribute centered loss for soft biometrics guided face sketch photo recognition"):
  with members:
    - "fingerprint distortion rectification using deep convolutional neural networks"
    - "deep cross polarimetric thermal to visible face recognition"
    - "contrastive center loss for deep neural networks"
    - "face attribute prediction using off the shelf cnn features"
    - "sparse graphical representation based discriminant analysis for heterogeneous face recognition"
    - "facenet a unified embedding for face recognition and clustering"
    - "very deep convolutional networks for large scale image recognition"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-3 (induced by source paper "learning a shared transform model for skull to digital face image matching"):
  with members:
    - "on matching skulls to digital face images a preliminary approach"

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)