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: "low shot learning with large scale diffusion"
  Abstract: This paper considers the problem of inferring image labels for which only a few labelled examples are available at training time. This setup is often referred to as low-shot learning in the literature, where a standard approach is to re-train the last few layers of a convolutional neural network learned on separate classes. We consider a semi-supervised setting in which we exploit a large collection of images to support label propagation. This is made possible by leveraging the recent advances on large-scale similarity graph construction. We show that despite its conceptual simplicity, scaling up label propagation to up hundred millions of images leads to state of the art accuracy in the low-shot learning regime.

Connected through hyperedge HE-1 (induced by source paper "semi supervised few shot learning with prototypical random walks"):
  with members:
    - "edge labeling graph neural network for few shot learning"
    - "learning to propagate labels transductive propagation network for few shot learning"
    - "semi supervised learning via compact latent space clustering"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "good semi supervised learning that requires a bad gan"
    - "prototypical networks for few shot learning"
    - "learning to compare relation network for few shot learning"
    - "a study and comparison of human and deep learning recognition performance under visual distortions"

Connected through hyperedge HE-2 (induced by source paper "class regularization improve few shot image classification by reducing meta shift"):
  with members:
    - "meta learning with differentiable convex optimization"
    - "generalizing from a few examples a survey on few shot learning"
    - "revisiting local descriptor based image to class measure for few shot learning"
    - "finding task relevant features for few shot learning by category traversal"
    - "a closer look at few shot classification"
    - "few shot learning with metric agnostic conditional embeddings"
    - "on first order meta learning algorithms"
    - "reptile a scalable metalearning algorithm"

Connected through hyperedge HE-3 (induced by source paper "graph convolutional networks for learning with few clean and many noisy labels"):
  with members:
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "a closer look at few shot classification"
    - "learning to propagate labels transductive propagation network for few shot learning"
    - "iterative learning with open set noisy labels"
    - "probabilistic model agnostic meta learning"
    - "dynamic few shot visual learning without forgetting"
    - "cleannet transfer learning for scalable image classifier training with label noise"
    - "low shot learning with imprinted weights"

Connected through hyperedge HE-4 (induced by source paper "billion scale semi supervised learning for image classification"):
  with members:
    - "rethinking imagenet pre training"
    - "low shot learning via covariance preserving adversarial augmentation networks"
    - "autoaugment learning augmentation policies from data"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "exploring the limits of weakly supervised pretraining"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "revisiting the effectiveness of off the shelf temporal modeling approaches for large scale video classification"

Connected through hyperedge HE-5 (induced by source paper "knowledge graph transfer network for few shot recognition"):
  with members:
    - "knowledge embedded routing network for scene graph generation"
    - "variational prototyping encoder one shot learning with prototypical images"
    - "learning semantic specific graph representation for multi label image recognition"
    - "knowledge embedded representation learning for fine grained image recognition"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "deep reasoning with knowledge graph for social relationship understanding"
    - "hybrid knowledge routed modules for large scale object detection"
    - "visual semantic navigation using scene priors"

Connected through hyperedge HE-6 (induced by source paper "label propagation for deep semi supervised learning"):
  with members:
    - "deep clustering for unsupervised learning of visual features"
    - "mining on manifolds metric learning without labels"
    - "transductive label augmentation for improved deep network learning"
    - "deep co training for semi supervised image recognition"
    - "unsupervised representation learning by predicting image rotations"
    - "dynamic few shot visual learning without forgetting"
    - "transitive invariance for self supervised visual representation learning"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"

Connected through hyperedge HE-7 (induced by source paper "low shot learning from imaginary 3d model"):
  with members:
    - "self paced deep learning for weakly supervised object detection"
    - "learning category specific mesh reconstruction from image collections"
    - "low shot learning from imaginary data"
    - "prototypical networks for few shot learning"
    - "bridging saliency detection to weakly supervised object detection based on self paced curriculum learning"
    - "keep it smpl automatic estimation of 3d human pose and shape from a single image"
    - "learning feed forward one shot learners"
    - "3d menagerie modeling the 3d shape and pose of animals"

Connected through hyperedge HE-8 (induced by source paper "pyramid a general framework for distributed similarity search"):
  with members:
    - "norm ranging lsh for maximum inner product search"
    - "link and code fast indexing with graphs and compact regression codes"
    - "revisiting the inverted indices for billion scale approximate nearest neighbors"
    - "billion scale similarity search with gpus"
    - "fast approximate nearest neighbor search with navigating spreading out graphs"
    - "a survey on learning to hash"
    - "clustering is efficient for approximate maximum inner product search"
    - "fast neighborhood graph search using cartesian concatenation"

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)