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: "secure face matching using fully homomorphic encryption"
  Abstract: Face recognition technology has demonstrated tremendous progress over the past few years, thanks to advances in representation learning. As we witness the widespread adoption of these systems, it is imperative to consider the security of face representations. In this paper, we propose a data encryption based framework to secure databases of face representations, with the goal of preventing information leakage and preserving the privacy of users, while maintaining it's utility. Specifically, we explore the possibility of using a fully homomorphic encryption based scheme for matching face representations directly in the encrypted domain along with a dimensionality reduction scheme to trade-off face matching accuracy and computational complexity. Experiments on benchmark face datasets (LFW, IJB-A, IJB-B, CASIA) indicate that secure face matching is practically feasible (0.01 sec per match pair for 512-dimensional features from SphereFace) while exhibiting minimal loss in matching performance.

Connected through hyperedge HE-1 (induced by source paper "learning a fixed length fingerprint representation"):
  with members:
    - "automatic latent fingerprint segmentation"
    - "multi task convolutional neural network for pose invariant face recognition"
    - "end to end latent fingerprint search"
    - "automated latent fingerprint recognition"
    - "robust minutiae extractor integrating deep networks and fingerprint domain knowledge"
    - "fingernet an unified deep network for fingerprint minutiae extraction"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "spatial transformer networks"

Connected through hyperedge HE-2 (induced by source paper "on the intrinsic dimensionality of face representation"):
  with members:
    - "sphereface deep hypersphere embedding for face recognition"
    - "sgdr stochastic gradient descent with restarts"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "identity mappings in deep residual networks"
    - "facenet a unified embedding for face recognition and clustering"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "learning face representation from scratch"

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)