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: "gaussian prototypical networks for few shot learning on omniglot"
  Abstract: We propose a novel architecture for k-shot classification on the Omniglot dataset. Building on prototypical networks, we extend their architecture to what we call Gaussian prototypical networks. Prototypical networks learn a map between images and embedding vectors, and use their clustering for classification. In our model, a part of the encoder output is interpreted as a confidence region estimate about the embedding point, and expressed as a Gaussian covariance matrix. Our network then constructs a direction and class dependent distance metric on the embedding space, using uncertainties of individual data points as weights. We show that Gaussian prototypical networks are a preferred architecture over vanilla prototypical networks with an equivalent number of parameters. We report results consistent with state-of-the-art performance in 1-shot and 5-shot classification both in 5-way and 20-way regime on the Omniglot dataset. We explore artificially down-sampling a fraction of images in the training set, which improves our performance. Our experiments therefore lead us to hypothesize that Gaussian prototypical networks might perform better in less homogeneous, noisier datasets, which are commonplace in real world applications.

Connected through hyperedge HE-1 (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"
    - "probabilistic model agnostic meta learning"
    - "large margin few shot learning"
    - "heated up softmax embedding"

Connected through hyperedge HE-2 (induced by source paper "one way prototypical networks"):
  with members:
    - "one class convolutional neural network"
    - "deep learning for anomaly detection a survey"
    - "learning deep features for one class classification"
    - "adversarially learned one class classifier for novelty detection"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "prototypical networks for few shot learning"
    - "matching networks for one shot learning"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-3 (induced by source paper "mxml mixture of meta learners for few shot classification"):
  with members:
    - "on first order meta learning algorithms"
    - "set transformer a framework for attention based permutation invariant neural networks"
    - "deep sets"
    - "prototypical networks for few shot learning"
    - "zero shot learning a comprehensive evaluation of the good the bad and the ugly"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "matching networks for one shot learning"
    - "on large batch training for deep learning generalization gap and sharp minima"

Connected through hyperedge HE-4 (induced by source paper "large margin few shot learning"):
  with members:
    - "arcface additive angular margin loss for deep face recognition"
    - "additive margin softmax for face verification"
    - "prototypical networks for few shot learning"
    - "rethinking feature discrimination and polymerization for large scale recognition"
    - "l2 constrained softmax loss for discriminative face verification"
    - "generative adversarial residual pairwise networks for one shot learning"
    - "meta sgd learning to learn quickly for few shot learning"
    - "learning to compare relation network for few shot learning"

Connected through hyperedge HE-5 (induced by source paper "prototypical clustering networks for dermatological disease diagnosis"):
  with members:
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "the devil is in the tails fine grained classification in the wild"
    - "prototypical networks for few shot learning"
    - "one shot learning with memory augmented neural networks"
    - "densely connected convolutional networks"
    - "matching networks for one shot learning"
    - "low shot visual recognition by shrinking and hallucinating features"
    - "deep residual learning for image recognition"

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)