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: "rethink and redesign meta learning"
  Abstract: Recently, meta-learning has shown as a promising way to improve the ability to learn from few-data for many computer vision tasks. #R##N#However, existing meta-learning approaches still fall behind human greatly, and like many deep learning algorithms, they also suffer from overfitting. We named this problem as Task-Over-Fitting (TOF) problem that the meta-learner over-fits to the training tasks, not to the training data. #R##N#We human beings can learn from few-data, mainly due to that we are so smart to leverage past knowledge to understand the images of new categories rapidly. Furthermore, be benefiting from our flexible attention mechanism, we can accurately extract and select key features from images and further solve few-shot learning tasks with excellent performance. #R##N#In this paper, we rethink the meta-learning algorithm and find that existing meta-learning approaches miss considering attention mechanism and past knowledge. #R##N#To this end, we present a novel paradigm of meta-learning approach with three developments to introduce attention mechanism and past knowledge step by step. #R##N#In this way, we can narrow the problem space and improve the performance of meta-learning, and the TOF problem can also be significantly reduced. #R##N#Extensive experiments demonstrate the effectiveness of our designation and methods with state-of-the-art performance not only on several few-shot learning benchmarks but also on the Cross-Entropy across Tasks (CET) metric.

Connected through hyperedge HE-1 (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"
    - "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"
    - "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-2 (induced by source paper "learning meta model for zero and few shot face anti spoofing"):
  with members:
    - "deep tree learning for zero shot face anti spoofing"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "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"
    - "a performance evaluation of convolutional neural networks for face anti spoofing"
    - "prototypical networks for few shot learning"
    - "a simple neural attentive meta learner"

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)