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: "learning structure and strength of cnn filters for small sample size training"
  Abstract: Convolutional Neural Networks have provided state-of-the-art results in several computer vision problems. However, due to a large number of parameters in CNNs, they require a large number of training samples which is a limiting factor for small sample size problems. To address this limitation, we propose SSF-CNN which focuses on learning the structure and strength of filters. The structure of the filter is initialized using a dictionary-based filter learning algorithm and the strength of the filter is learned using the small sample training data. The architecture provides the flexibility of training with both small and large training databases and yields good accuracies even with small size training data. The effectiveness of the algorithm is first demonstrated on MNIST, CIFAR10, and NORB databases, with a varying number of training samples. The results show that SSF-CNN significantly reduces the number of parameters required for training while providing high accuracies the test databases. On small sample size problems such as newborn face recognition and Omniglot, it yields state-of-the-art results. Specifically, on the IIITD Newborn Face Database, the results demonstrate improvement in rank-1 identification accuracy by at least 10%.

Connected through hyperedge HE-1 (induced by source paper "deepemd few shot image classification with differentiable earth mover s distance and structured classifiers"):
  with members:
    - "dense classification and implanting for few shot learning"
    - "few shot learning via saliency guided hallucination of samples"
    - "meta learning with differentiable convex optimization"
    - "few shot learning with localization in realistic settings"
    - "learning from adversarial features for few shot classification"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "meta learning with warped gradient descent"
    - "edge labeling graph neural network for few shot learning"

Connected through hyperedge HE-2 (induced by source paper "meta transfer learning through hard tasks"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "lgm net learning to generate matching networks for few shot learning"
    - "a closer look at few shot classification"
    - "recasting gradient based meta learning as hierarchical bayes"
    - "adapted deep embeddings a synthesis of methods for k shot inductive transfer learning"
    - "meta learning with latent embedding optimization"
    - "learning to propagate labels transductive propagation network for few shot learning"

Connected through hyperedge HE-3 (induced by source paper "extension of convolutional neural network with general image processing kernels"):
  with members:
    - "faster r cnn towards real time object detection with region proposal networks"

Connected through hyperedge HE-4 (induced by source paper "meta transfer learning for few shot learning"):
  with members:
    - "recasting gradient based meta learning as hierarchical bayes"
    - "adapted deep embeddings a synthesis of methods for k shot inductive transfer learning"
    - "meta learning with latent embedding optimization"
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "taskonomy disentangling task transfer learning"
    - "delta encoder an effective sample synthesis method for few shot object recognition"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "low shot learning from imaginary data"

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)