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: "attentive recurrent comparators"
  Abstract: Rapid learning requires flexible representations to quickly adopt to new evidence. We develop a novel class of models called Attentive Recurrent Comparators (ARCs) that form representations of objects by cycling through them and making observations. Using the representations extracted by ARCs, we develop a way of approximating a \textit{dynamic representation space} and use it for one-shot learning. In the task of one-shot classification on the Omniglot dataset, we achieve the state of the art performance with an error rate of 1.5\%. This represents the first super-human result achieved for this task with a generic model that uses only pixel information.

Connected through hyperedge HE-1 (induced by source paper "a two stage approach to few shot learning for image recognition"):
  with members:
    - "generalizing from a few examples a survey on few shot learning"
    - "adaptive posterior learning few shot learning with a surprise based memory module"
    - "few shot learning with metric agnostic conditional embeddings"
    - "on first order meta learning algorithms"
    - "sample to sample correspondence for unsupervised domain adaptation"
    - "learning to propagate labels transductive propagation network for few shot learning"
    - "unsupervised domain adaptation using regularized hyper graph matching"
    - "meta transfer learning for few shot learning"

Connected through hyperedge HE-2 (induced by source paper "automatic detection of rare pathologies in fundus photographs using few shot learning"):
  with members:
    - "cats or cat scans transfer learning from natural or medical image source data sets"
    - "generalizing from a few examples a survey on few shot learning"
    - "multi task learning for chest x ray abnormality classification on noisy labels"
    - "deep image mining for diabetic retinopathy screening"
    - "learning transferable architectures for scalable image recognition"
    - "prototypical networks for few shot learning"
    - "learning to compare relation network for few shot learning"
    - "a survey on deep learning in medical image analysis"

Connected through hyperedge HE-3 (induced by source paper "meta r cnn towards general solver for instance level few shot learning"):
  with members:
    - "provable guarantees for gradient based meta learning"
    - "panoptic feature pyramid networks"
    - "few shot object detection via feature reweighting"
    - "low shot learning from imaginary data"
    - "lstd a low shot transfer detector for object detection"
    - "dynamic few shot visual learning without forgetting"
    - "detecting and recognizing human object interactions"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-4 (induced by source paper "saliency for fine grained object recognition in domains with scarce training data"):
  with members:
    - "large scale fine grained categorization and domain specific transfer learning"
    - "deep visual attention prediction"
    - "feature space transfer for data augmentation"
    - "dynamic few shot visual learning without forgetting"
    - "deep layer aggregation"
    - "prototypical networks for few shot learning"
    - "exploiting inter image similarity and ensemble of extreme learners for fixation prediction using deep features"
    - "fine grained categorization via cnn based automatic extraction and integration of object level and part level features"

Connected through hyperedge HE-5 (induced by source paper "graph matching networks for learning the similarity of graph structured objects"):
  with members:
    - "a survey on graph kernels"
    - "ddgk learning graph representations for deep divergence graph kernels"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "deep sets"
    - "learning deep generative models of graphs"
    - "relational inductive biases deep learning and graph networks"
    - "dynamic graph cnn for learning on point clouds"
    - "how powerful are graph neural networks"

Connected through hyperedge HE-6 (induced by source paper "the omniglot challenge a 3 year progress report"):
  with members:
    - "the variational homoencoder learning to learn high capacity generative models from few examples"
    - "synthesizing programs for images using reinforced adversarial learning"
    - "active one shot learning"
    - "prototypical networks for few shot learning"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "scaling memory augmented neural networks with sparse reads and writes"
    - "matching networks for one shot learning"
    - "building machines that learn and think like people"

Connected through hyperedge HE-7 (induced by source paper "automatic detection of multiple pathologies in fundus photographs using spin off learning"):
  with members:
    - "cats or cat scans transfer learning from natural or medical image source data sets"
    - "multi task learning for chest x ray abnormality classification on noisy labels"
    - "deep image mining for diabetic retinopathy screening"
    - "learning transferable architectures for scalable image recognition"
    - "a survey on deep learning in medical image analysis"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "deep residual learning for image recognition"
    - "particular object retrieval with integral max pooling of cnn activations"

Connected through hyperedge HE-8 (induced by source paper "targeted kernel networks faster convolutions with attentive regularization"):
  with members:
    - "sbnet sparse blocks network for fast inference"
    - "non local neural networks"
    - "dynamic routing between capsules"
    - "eac net a region based deep enhancing and cropping approach for facial action unit detection"
    - "interpretable convolutional neural networks"
    - "attentional pooling for action recognition"
    - "more is less a more complicated network with less inference complexity"
    - "a survey of model compression and acceleration for deep neural networks"

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)