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: "large margin few shot learning"
  Abstract: The key issue of few-shot learning is learning to generalize. This paper proposes a large margin principle to improve the generalization capacity of metric based methods for few-shot learning. To realize it, we develop a unified framework to learn a more discriminative metric space by augmenting the classification loss function with a large margin distance loss function for training. Extensive experiments on two state-of-the-art few-shot learning methods, graph neural networks and prototypical networks, show that our method can improve the performance of existing models substantially with very little computational overhead, demonstrating the effectiveness of the large margin principle and the potential of our method.

Connected through hyperedge HE-1 (induced by source paper "associative alignment for few shot image classification"):
  with members:
    - "cross domain few shot classification via learned feature wise transformation"
    - "dense classification and implanting for few shot learning"
    - "variational prototyping encoder one shot learning with prototypical images"
    - "few shot learning via saliency guided hallucination of samples"
    - "a baseline for few shot image classification"
    - "meta learning with differentiable convex optimization"
    - "few shot learning with localization in realistic settings"
    - "boosting few shot visual learning with self supervision"

Connected through hyperedge HE-2 (induced by source paper "revisiting metric learning for few shot image classification"):
  with members:
    - "dense classification and implanting for few shot learning"
    - "multi level semantic feature augmentation for one shot learning"
    - "meta learning with differentiable convex optimization"
    - "lgm net learning to generate matching networks for few shot learning"
    - "few shot learning with localization in realistic settings"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "edge labeling graph neural network for few shot learning"
    - "finding task relevant features for few shot learning by category traversal"

Connected through hyperedge HE-3 (induced by source paper "comparison network for one shot conditional object detection"):
  with members:
    - "meta learning with latent embedding optimization"
    - "zero shot object detection by hybrid region embedding"
    - "metalearning with hebbian fast weights"
    - "focal loss for dense object detection"
    - "prototypical networks for few shot learning"
    - "learning to compare relation network for few shot learning"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "weakly supervised object localization with multi fold multiple instance learning"

Connected through hyperedge HE-4 (induced by source paper "variational metric scaling for metric based meta learning"):
  with members:
    - "finding task relevant features for few shot learning by category traversal"
    - "on first order meta learning algorithms"
    - "rethinking feature distribution for loss functions in image classification"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "few shot object detection via feature reweighting"
    - "gaussian prototypical networks for few shot learning on omniglot"
    - "probabilistic model agnostic meta learning"
    - "heated up softmax embedding"

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.LG(Machine Learning)