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: "exploiting temporal and depth information for multi frame face anti spoofing"
  Abstract: Face anti-spoofing is significant to the security of face recognition systems. Previous works on depth supervised learning have proved the effectiveness for face anti-spoofing. Nevertheless, they only considered the depth as an auxiliary supervision in the single frame. Different from these methods, we develop a new method to estimate depth information from multiple RGB frames and propose a depth-supervised architecture which can efficiently encodes spatiotemporal information for presentation attack detection. It includes two novel modules: optical flow guided feature block (OFFB) and convolution gated recurrent units (ConvGRU) module, which are designed to extract short-term and long-term motion to discriminate living and spoofing faces. Extensive experiments demonstrate that the proposed approach achieves state-of-the-art results on four benchmark datasets, namely OULU-NPU, SiW, CASIA-MFSD, and Replay-Attack.

Connected through hyperedge HE-1 (induced by source paper "searching central difference convolutional networks for face anti spoofing"):
  with members:
    - "video action recognition via neural architecture searching"
    - "pc darts partial channel connections for memory efficient differentiable architecture search"
    - "deep pixel wise binary supervision for face presentation attack detection"
    - "deep tree learning for zero shot face anti spoofing"
    - "neural architecture search for deep face recognition"
    - "searching for a robust neural architecture in four gpu hours"
    - "customizable architecture search for semantic segmentation"
    - "auto reid searching for a part aware convnet for person re identification"

Connected through hyperedge HE-2 (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"
    - "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"

Connected through hyperedge HE-3 (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"
    - "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-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"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "a dataset and benchmark for large scale multi modal face anti spoofing"
    - "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"
    - "a dataset and benchmark for large scale multi modal 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 "casia surf a large scale multi modal benchmark for face anti spoofing"):
  with members:
    - "refineface refinement neural network for high performance face detection"
    - "mis classified vector guided softmax loss for face recognition"
    - "improved selective refinement network for face detection"
    - "face de spoofing anti spoofing via noise modeling"
    - "ensemble soft margin softmax loss for image classification"
    - "selective refinement network for high performance face detection"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "support vector guided softmax loss for face recognition"

Connected through hyperedge HE-7 (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"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "on the learning of deep local features for robust face spoofing detection"
    - "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-8 (induced by source paper "static and dynamic fusion for multi modal cross ethnicity face anti spoofing"):
  with members:
    - "casia surf a large scale multi modal benchmark for face anti spoofing"
    - "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"
    - "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"

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)