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: "biometric face presentation attack detection with multi channel convolutional neural network"
  Abstract: Face recognition is a mainstream biometric authentication method. However, the vulnerability to presentation attacks (a.k.a. spoofing) limits its usability in unsupervised applications. Even though there are many methods available for tackling presentation attacks (PA), most of them fail to detect sophisticated attacks such as silicone masks. As the quality of presentation attack instruments improves over time, achieving reliable PA detection with visual spectra alone remains very challenging. We argue that analysis in multiple channels might help to address this issue. In this context, we propose a multi-channel Convolutional Neural Network-based approach for presentation attack detection (PAD). We also introduce the new Wide Multi-Channel presentation Attack (WMCA) database for face PAD which contains a wide variety of 2D and 3D presentation attacks for both impersonation and obfuscation attacks. Data from different channels such as color, depth, near-infrared, and thermal are available to advance the research in face PAD. The proposed method was compared with feature-based approaches and found to outperform the baselines achieving an ACER of 0.3% on the introduced dataset. The database and the software to reproduce the results are made available publicly.

Connected through hyperedge HE-1 (induced by source paper "face anti spoofing by learning polarization cues in a real world scenario"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "face de spoofing anti spoofing via noise modeling"
    - "a dataset and benchmark for large scale multi modal face anti spoofing"
    - "paraph presentation attack rejection by analyzing polarization hypotheses"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-2 (induced by source paper "demographic bias in presentation attack detection of iris recognition systems"):
  with members:
    - "post comparison mitigation of demographic bias in face recognition using fair score normalization"
    - "an experimental evaluation of covariates effects on unconstrained face verification"
    - "presentation attack detection for iris recognition an assessment of the state of the art"
    - "very deep convolutional networks for large scale image recognition"

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

Connected through hyperedge HE-4 (induced by source paper "facial behavior analysis using 4d curvature statistics for presentation attack detection"):
  with members:
    - "neural voice cloning with a few samples"
    - "a dataset and benchmark for large scale multi modal face anti spoofing"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"

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)