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: "casia surf a large scale multi modal benchmark for 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 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 comprehensive evaluation metrics, diverse evaluation protocols, training/validation/testing subsets and a measurement tool, developing a new benchmark for face anti-spoofing. Moreover, we present a novel multi-modal multi-scale fusion method as a strong baseline, which performs feature re-weighting to select the more informative channel features while suppressing the less useful ones for each modality across different scales. 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 "casia surf cefa a benchmark for multi modal cross ethnicity face anti spoofing"):
  with members:
    - "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"
    - "a dataset and benchmark for large scale multi modal 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-2 (induced by source paper "static and dynamic fusion for multi modal cross ethnicity face anti spoofing"):
  with members:
    - "deep tree learning for zero shot face anti spoofing"
    - "face alignment in full pose range a 3d total solution"
    - "feathernets convolutional neural networks as light as feather for face anti spoofing"
    - "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"
    - "cooperative training of deep aggregation networks for rgb d action recognition"
    - "rank pooling for action recognition"

Connected through hyperedge HE-3 (induced by source paper "attention based face antispoofing of rgb images using a minimal end 2 end neural network"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "on the variance of the adaptive learning rate and beyond"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "group normalization"
    - "swish a self gated activation function"
    - "xception deep learning with depthwise separable convolutions"
    - "joint face detection and alignment using multitask cascaded convolutional networks"

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)