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 dataset and benchmark for large scale multi modal face anti spoofing"
  Abstract: Face anti-spoofing is essential to prevent face recognition systems from a security breach. Much of the progresses have been made by the availability of face anti-spoofing benchmark datasets in recent years. However, existing face anti-spoofing benchmarks have limited number of subjects ($\le\negmedspace170$) and modalities ($\leq\negmedspace2$), which hinder the further development of the academic community. To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIA-SURF, which is the largest publicly available dataset for face anti-spoofing in terms of both subjects and visual modalities. Specifically, it consists of $1,000$ subjects with $21,000$ videos and each sample has $3$ modalities (i.e., RGB, Depth and IR). We also provide a measurement set, evaluation protocol and training/validation/testing subsets, developing a new benchmark for face anti-spoofing. Moreover, we present a new multi-modal fusion method as baseline, which performs feature re-weighting to select the more informative channel features while suppressing the less useful ones for each modal. Extensive experiments have been conducted on the proposed dataset to verify its significance and generalization capability. The dataset is available at this https URL

Connected through hyperedge HE-1 (induced by source paper "deep frequent spatial temporal learning for face anti spoofing"):
  with members:
    - "specular and diffuse reflection based face liveness detection for mobile devices"
    - "learning generalizable and identity discriminative representations for face anti spoofing"
    - "feathernets convolutional neural networks as light as feather for face anti spoofing"
    - "face de spoofing anti spoofing via noise modeling"
    - "discriminative representation combinations for accurate face spoofing detection"
    - "exploiting temporal and depth information for multi frame face anti spoofing"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"

Connected through hyperedge HE-2 (induced by source paper "casia surf cefa a benchmark for multi modal cross ethnicity face anti spoofing"):
  with members:
    - "casia surf a large scale multi modal benchmark for face anti spoofing"
    - "face alignment in full pose range a 3d total solution"
    - "face de spoofing anti spoofing via noise modeling"
    - "exploiting temporal and depth information for multi frame face anti spoofing"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "racial faces in the wild reducing racial bias by information maximization adaptation network"
    - "cooperative training of deep aggregation networks for rgb d action recognition"

Connected through hyperedge HE-3 (induced by source paper "face anti spoofing by learning polarization cues in a real world scenario"):
  with members:
    - "biometric face presentation attack detection with multi channel convolutional neural network"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "face de spoofing anti spoofing via noise modeling"
    - "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-4 (induced by source paper "feathernets convolutional neural networks as light as feather for face anti spoofing"):
  with members:
    - "igcv3 interleaved low rank group convolutions for efficient deep neural networks"
    - "bag of tricks for image classification with convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "arcface additive angular margin loss for deep face recognition"
    - "exploiting temporal and depth information for multi frame face anti spoofing"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "mobilefacenets efficient cnns for accurate real time face verification on mobile devices"
    - "joint 3d face reconstruction and dense alignment with position map regression network"

Connected through hyperedge HE-5 (induced by source paper "meta anti spoofing learning to learn in face anti spoofing"):
  with members:
    - "recasting gradient based meta learning as hierarchical bayes"
    - "exploiting temporal and depth information for multi frame face anti spoofing"
    - "rethink and redesign meta learning"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "a performance evaluation of convolutional neural networks for face anti spoofing"
    - "attention is all you need"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-6 (induced by source paper "generalized presentation attack detection a face anti spoofing evaluation proposal"):
  with members:
    - "exploiting temporal and depth information for multi frame face anti spoofing"
    - "large pose 3d face reconstruction from a single image via direct volumetric cnn regression"
    - "joint face detection and alignment using multitask cascaded convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "facial behavior analysis using 4d curvature statistics for presentation attack detection"):
  with members:
    - "biometric face presentation attack detection with multi channel convolutional neural network"
    - "neural voice cloning with a few samples"
    - "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)