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: "on the learning of deep local features for robust face spoofing detection"
  Abstract: Biometrics emerged as a robust solution for security systems. However, given the dissemination of biometric applications, criminals are developing techniques to circumvent them by simulating physical or behavioral traits of legal users (spoofing attacks). Despite face being a promising characteristic due to its universality, acceptability and presence of cameras almost everywhere, face recognition systems are extremely vulnerable to such frauds since they can be easily fooled with common printed facial photographs. State-of-the-art approaches, based on Convolutional Neural Networks (CNNs), present good results in face spoofing detection. However, these methods do not consider the importance of learning deep local features from each facial region, even though it is known from face recognition that each facial region presents different visual aspects, which can also be exploited for face spoofing detection. In this work we propose a novel CNN architecture trained in two steps for such task. Initially, each part of the neural network learns features from a given facial region. Afterwards, the whole model is fine-tuned on the whole facial images. Results show that such pre-training step allows the CNN to learn different local spoofing cues, improving the performance and the convergence speed of the final model, outperforming the state-of-the-art approaches.

Connected through hyperedge HE-1 (induced by source paper "improving face anti spoofing by 3d virtual synthesis"):
  with members:
    - "face alignment in full pose range a 3d total solution"
    - "face synthesis for eyeglass robust face recognition"
    - "exploiting temporal and depth information for multi frame face anti spoofing"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "a performance evaluation of convolutional neural networks for face anti spoofing"
    - "learning camera viewpoint using cnn to improve 3d body pose estimation"
    - "mocap guided data augmentation for 3d pose estimation in the wild"
    - "synthesizing training images for boosting human 3d pose estimation"

Connected through hyperedge HE-2 (induced by source paper "unseen face presentation attack detection using class specific sparse one class multiple kernel fusion regression"):
  with members:
    - "biometric presentation attack detection beyond the visible spectrum"
    - "deep pixel wise binary supervision for face presentation attack detection"
    - "deep anomaly detection for generalized face anti spoofing"
    - "deep tree learning for zero shot face anti spoofing"
    - "neural class specific regression for face verification"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "deep residual learning for image recognition"
    - "face anti spoofing based on color texture analysis"

Connected through hyperedge HE-3 (induced by source paper "domain adaptation in multi channel autoencoder based features for robust face anti spoofing"):
  with members:
    - "biometric face presentation attack detection with multi channel convolutional neural network"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "detecting anomalous faces with no peeking autoencoders"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "deep learning face attributes in the wild"

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)