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: "power normalizing second order similarity network for few shot learning"
  Abstract: Second- and higher-order statistics of data points have played an important role in advancing the state of the art on several computer vision problems such as the fine-grained image and scene recognition. However, these statistics need to be passed via an appropriate pooling scheme to obtain the best performance. Power Normalizations are non-linear activation units which enjoy probability-inspired derivations and can be applied in CNNs. In this paper, we propose a similarity learning network leveraging second-order information and Power Normalizations. To this end, we propose several formulations capturing second-order statistics and derive a sigmoid-like Power Normalizing function to demonstrate its interpretability. Our model is trained end-to-end to learn the similarity between the support set and query images for the problem of one- and few-shot learning. The evaluations on Omniglot, miniImagenet and Open MIC datasets demonstrate that this network obtains state-of-the-art results on several few-shot learning protocols.

Connected through hyperedge HE-1 (induced by source paper "learning to compare relation semantic alignment for few shot learning"):
  with members:
    - "a two stage approach to few shot learning for image recognition"
    - "dense classification and implanting for few shot learning"
    - "learning video representations using contrastive bidirectional transformer"
    - "f vaegan d2 a feature generating framework for any shot learning"
    - "few shot learning with localization in realistic settings"
    - "boosting few shot visual learning with self supervision"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "adaptive posterior learning few shot learning with a surprise based memory module"

Connected through hyperedge HE-2 (induced by source paper "few shot action recognition via improved attention with self supervision"):
  with members:
    - "few shot learning via saliency guided hallucination of samples"
    - "meta learning with differentiable convex optimization"
    - "few shot learning with localization in realistic settings"
    - "boosting few shot visual learning with self supervision"
    - "edge labeling graph neural network for few shot learning"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "boosting supervision with self supervision for few shot learning"
    - "moments in time dataset one million videos for event understanding"

Connected through hyperedge HE-3 (induced by source paper "few shot learning with multi scale self supervision"):
  with members:
    - "few shot learning via saliency guided hallucination of samples"
    - "meta learning with differentiable convex optimization"
    - "few shot learning with localization in realistic settings"
    - "edge labeling graph neural network for few shot learning"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "hallucinating idt descriptors and i3d optical flow features for action recognition with cnns"
    - "meta learning with latent embedding optimization"
    - "how to train your maml"

Connected through hyperedge HE-4 (induced by source paper "rethinking class relations absolute relative few shot learning"):
  with members:
    - "few shot learning via saliency guided hallucination of samples"
    - "meta learning with differentiable convex optimization"
    - "boosting few shot visual learning with self supervision"
    - "edge labeling graph neural network for few shot learning"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "how to train your maml"
    - "zero shot kernel learning"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-5 (induced by source paper "few shot learning with localization in realistic settings"):
  with members:
    - "deep covariance descriptors for facial expression recognition"
    - "covariance pooling for facial expression recognition"
    - "low shot learning from imaginary data"
    - "prototypical networks for few shot learning"
    - "extreme clicking for efficient object annotation"
    - "a simple neural attentive meta learner"
    - "residual attention network for image classification"
    - "the inaturalist species classification and detection dataset"

Connected through hyperedge HE-6 (induced by source paper "few shot learning via saliency guided hallucination of samples"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "zero shot kernel learning"
    - "a deeper look at power normalizations"
    - "low shot learning from imaginary data"
    - "model selection for generalized zero shot learning"
    - "deep unsupervised saliency detection a multiple noisy labeling perspective"
    - "a simple neural network module for relational reasoning"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-7 (induced by source paper "model selection for generalized zero shot learning"):
  with members:
    - "zero shot kernel learning"
    - "a deeper look at power normalizations"
    - "semantic autoencoder for zero shot learning"
    - "feature generating networks for zero shot learning"
    - "zero shot learning the good the bad and the ugly"
    - "synthesized classifiers for zero shot learning"
    - "latent embeddings for zero shot classification"
    - "evaluation of output embeddings for fine grained image classification"

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)