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: "memory matching networks for one shot image recognition"
  Abstract: In this paper, we introduce the new ideas of augmenting Convolutional Neural Networks (CNNs) with Memory and learning to learn the network parameters for the unlabelled images on the fly in one-shot learning. Specifically, we present Memory Matching Networks (MM-Net) --- a novel deep architecture that explores the training procedure, following the philosophy that training and test conditions must match. Technically, MM-Net writes the features of a set of labelled images (support set) into memory and reads from memory when performing inference to holistically leverage the knowledge in the set. Meanwhile, a Contextual Learner employs the memory slots in a sequential manner to predict the parameters of CNNs for unlabelled images. The whole architecture is trained by once showing only a few examples per class and switching the learning from minibatch to minibatch, which is tailored for one-shot learning when presented with a few examples of new categories at test time. Unlike the conventional one-shot learning approaches, our MM-Net could output one unified model irrespective of the number of shots and categories. Extensive experiments are conducted on two public datasets, i.e., Omniglot and \emph{mini}ImageNet, and superior results are reported when compared to state-of-the-art approaches. More remarkably, our MM-Net improves one-shot accuracy on Omniglot from 98.95% to 99.28% and from 49.21% to 53.37% on \emph{mini}ImageNet.

Connected through hyperedge HE-1 (induced by source paper "continual local replacement for few shot learning"):
  with members:
    - "a baseline for few shot image classification"
    - "meta learning with differentiable convex optimization"
    - "a closer look at few shot classification"
    - "image deformation meta networks for one shot learning"
    - "do better imagenet models transfer better"
    - "meta learning with latent embedding optimization"
    - "learning to propagate labels transductive propagation network for few shot learning"
    - "deep meta learning learning to learn in the concept space"

Connected through hyperedge HE-2 (induced by source paper "multi level semantic feature augmentation for one shot learning"):
  with members:
    - "deep meta learning learning to learn in the concept space"
    - "low shot learning from imaginary data"
    - "dynamic few shot visual learning without forgetting"
    - "prototypical networks for few shot learning"
    - "few shot image recognition by predicting parameters from activations"
    - "meta sgd learning to learn quickly for few shot learning"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "learning to compare relation network for few shot learning"

Connected through hyperedge HE-3 (induced by source paper "few shot object detection with attention rpn and multi relation detector"):
  with members:
    - "dense classification and implanting for few shot learning"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "few example object detection with model communication"
    - "edge labeling graph neural network for few shot learning"
    - "finding task relevant features for few shot learning by category traversal"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "meta r cnn towards general solver for instance level few shot learning"
    - "one shot segmentation in clutter"

Connected through hyperedge HE-4 (induced by source paper "cross attention network for few shot classification"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "on first order meta learning algorithms"
    - "bam bottleneck attention module"
    - "meta learning for semi supervised few shot classification"
    - "meta learning with latent embedding optimization"
    - "learning to propagate labels transductive propagation network for few shot learning"
    - "tadam task dependent adaptive metric for improved few shot learning"

Connected through hyperedge HE-5 (induced by source paper "few shot learning with global class representations"):
  with members:
    - "adaptive posterior learning few shot learning with a surprise based memory module"
    - "on first order meta learning algorithms"
    - "reptile a scalable metalearning algorithm"
    - "meta learning with latent embedding optimization"
    - "meta learning with differentiable closed form solvers"
    - "low shot learning from imaginary data"
    - "dynamic few shot visual learning without forgetting"
    - "a unified approach for conventional zero shot generalized zero shot and few shot learning"

Connected through hyperedge HE-6 (induced by source paper "dawn dual augmented memory network for unsupervised video object tracking"):
  with members:
    - "distractor aware siamese networks for visual object tracking"
    - "learning dynamic memory networks for object tracking"
    - "real time visual tracking by deep reinforced decision making"
    - "mavot memory augmented video object tracking"
    - "deep reinforcement learning for visual object tracking in videos"
    - "online multi object tracking using cnn based single object tracker with spatial temporal attention mechanism"
    - "multi context attention for human pose estimation"
    - "residual attention network for image classification"

Connected through hyperedge HE-7 (induced by source paper "image deformation meta networks for one shot learning"):
  with members:
    - "multi level semantic feature augmentation for one shot learning"
    - "low shot learning via covariance preserving adversarial augmentation networks"
    - "delta encoder an effective sample synthesis method for few shot object recognition"
    - "deep meta learning learning to learn in the concept space"
    - "low shot learning from imaginary data"
    - "dynamic few shot visual learning without forgetting"
    - "prototypical networks for few shot learning"
    - "mixup beyond empirical risk minimization"

Connected through hyperedge HE-8 (induced by source paper "progressive cluster purification for transductive few shot learning"):
  with members:
    - "meta learning for semi supervised few shot classification"
    - "learning to propagate labels transductive propagation network for few shot learning"
    - "low shot learning from imaginary data"
    - "attention is all you need"
    - "prototypical networks for few shot learning"
    - "few shot image recognition by predicting parameters from activations"
    - "learning to compare relation network 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)