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: "learn convolutional neural network for face anti spoofing"
  Abstract: Though having achieved some progresses, the hand-crafted texture features, e.g., LBP [23], LBP-TOP [11] are still unable to capture the most discriminative cues between genuine and fake faces. In this paper, instead of designing feature by ourselves, we rely on the deep convolutional neural network (CNN) to learn features of high discriminative ability in a supervised manner. Combined with some data pre-processing, the face anti-spoofing performance improves drastically. In the experiments, over 70% relative decrease of Half Total Error Rate (HTER) is achieved on two challenging datasets, CASIA [36] and REPLAY-ATTACK [7] compared with the state-of-the-art. Meanwhile, the experimental results from inter-tests between two datasets indicates CNN can obtain features with better generalization ability. Moreover, the nets trained using combined data from two datasets have less biases between two datasets.

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 spatial gradient and temporal depth learning for face anti spoofing"):
  with members:
    - "aurora guard real time face anti spoofing via light reflection"
    - "improving face anti spoofing by 3d virtual synthesis"
    - "exploring hypergraph representation on face anti spoofing beyond 2d attacks"
    - "discriminative representation combinations for accurate face spoofing detection"
    - "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"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "biometric face presentation attack detection with multi channel convolutional neural network"):
  with members:
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "lcnn low level feature embedded cnn for salient object detection"
    - "a light cnn for deep face representation with noisy labels"
    - "face anti spoofing based on color texture analysis"
    - "adam a method for stochastic optimization"
    - "how transferable are features in deep neural networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "use of a capsule network to detect fake images and videos"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "fsgan subject agnostic face swapping and reenactment"
    - "deferred neural rendering image synthesis using neural textures"
    - "multi task learning for detecting and segmenting manipulated facial images and videos"
    - "faceforensics learning to detect manipulated facial images"
    - "recurrent convolutional strategies for face manipulation detection in videos"
    - "domain adaptation in multi channel autoencoder based features for robust face anti spoofing"
    - "deep pixel wise binary supervision for face presentation attack detection"

Connected through hyperedge HE-5 (induced by source paper "regularized fine grained meta face anti spoofing"):
  with members:
    - "on first order meta learning algorithms"
    - "boosting domain adaptation by discovering latent domains"
    - "importance weighted adversarial nets for partial domain adaptation"
    - "deepjdot deep joint distribution optimal transport for unsupervised domain adaptation"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "maximum classifier discrepancy for unsupervised domain adaptation"
    - "learning to generalize meta learning for domain generalization"

Connected through hyperedge HE-6 (induced by source paper "domain agnostic feature learning for image and video based face anti spoofing"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deep transfer across domains for face antispoofing"
    - "facespoof buster a presentation attack detector based on intrinsic image properties and deep learning"
    - "learning generalizable and identity discriminative representations for face anti spoofing"
    - "learning deep models for face anti spoofing binary or auxiliary supervision"
    - "unified deep supervised domain adaptation and generalization"
    - "deeper broader and artier domain generalization"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (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"
    - "exploiting temporal and depth information for multi frame face anti spoofing"
    - "selective refinement network for high performance face detection"
    - "joint 3d face reconstruction and dense alignment with position map regression network"

Connected through hyperedge HE-8 (induced by source paper "attacking cnn based anti spoofing face authentication in the physical domain"):
  with members:
    - "face de spoofing anti spoofing via noise modeling"
    - "physical adversarial examples for object detectors"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "synthesizing robust adversarial examples"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"

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)