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: "learning to remember rare events"
  Abstract: Despite recent advances, memory-augmented deep neural networks are still limited when it comes to life-long and one-shot learning, especially in remembering rare events. We present a large-scale life-long memory module for use in deep learning. The module exploits fast nearest-neighbor algorithms for efficiency and thus scales to large memory sizes. Except for the nearest-neighbor query, the module is fully differentiable and trained end-to-end with no extra supervision. It operates in a life-long manner, i.e., without the need to reset it during training. #R##N#Our memory module can be easily added to any part of a supervised neural network. To show its versatility we add it to a number of networks, from simple convolutional ones tested on image classification to deep sequence-to-sequence and recurrent-convolutional models. In all cases, the enhanced network gains the ability to remember and do life-long one-shot learning. Our module remembers training examples shown many thousands of steps in the past and it can successfully generalize from them. We set new state-of-the-art for one-shot learning on the Omniglot dataset and demonstrate, for the first time, life-long one-shot learning in recurrent neural networks on a large-scale machine translation task.

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 "learning to memorize in neural task oriented dialogue systems"):
  with members:
    - "transferable multi domain state generator for task oriented dialogue systems"
    - "zero shot dialog generation with cross domain latent actions"
    - "skeleton to response dialogue generation guided by retrieval memory"
    - "the natural language decathlon multitask learning as question answering"
    - "an end to end approach for handling unknown slot values in dialogue state tracking"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "emo2vec learning generalized emotion representation by multi task training"
    - "global locally self attentive dialogue state tracker"

Connected through hyperedge HE-3 (induced by source paper "boosting few shot visual learning with self supervision"):
  with members:
    - "dense classification and implanting for few shot learning"
    - "generating classification weights with gnn denoising autoencoders for few shot learning"
    - "revisiting self supervised visual representation learning"
    - "meta learning for semi supervised few shot classification"
    - "meta learning with latent embedding optimization"
    - "learning to propagate labels transductive propagation network for few shot learning"
    - "unsupervised learning via meta learning"
    - "tadam task dependent adaptive metric for improved few shot learning"

Connected through hyperedge HE-4 (induced by source paper "generating classification weights with gnn denoising autoencoders for few shot learning"):
  with members:
    - "meta learning for semi supervised few shot classification"
    - "zero shot recognition via semantic embeddings and knowledge graphs"
    - "meta learning with latent embedding optimization"
    - "learning to propagate labels transductive propagation network for few shot learning"
    - "tadam task dependent adaptive metric for improved few shot learning"
    - "rethinking knowledge graph propagation for zero shot learning"
    - "meta learning with differentiable closed form solvers"
    - "low shot learning from imaginary data"

Connected through hyperedge HE-5 (induced by source paper "few shot video classification via temporal alignment"):
  with members:
    - "a closer look at few shot classification"
    - "d3tw discriminative differentiable dynamic time warping for weakly supervised action alignment and segmentation"
    - "a generative approach to zero shot and few shot action recognition"
    - "eco efficient convolutional network for online video understanding"
    - "on first order meta learning algorithms"
    - "reptile a scalable metalearning algorithm"
    - "meta learning with latent embedding optimization"
    - "neuralnetwork viterbi a framework for weakly supervised video learning"

Connected through hyperedge HE-6 (induced by source paper "large scale historical watermark recognition dataset and a new consistency based approach"):
  with members:
    - "discovering visual patterns in art collections with spatially consistent feature learning"
    - "virtual training for a real application accurate object robot relative localization without calibration"
    - "sketchmate deep hashing for million scale human sketch retrieval"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "neighbourhood consensus networks"
    - "identifying cross depicted historical motifs"
    - "low shot learning from imaginary data"
    - "dynamic few shot visual learning without forgetting"

Connected through hyperedge HE-7 (induced by source paper "learning from web data with memory module"):
  with members:
    - "symmetric cross entropy for robust learning with noisy labels"
    - "nlnl negative learning for noisy labels"
    - "probabilistic end to end noise correction for learning with noisy labels"
    - "curriculumnet weakly supervised learning from large scale web images"
    - "attention based deep multiple instance learning"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "learning from noisy web data with category level supervision"
    - "learning to learn from noisy labeled data"

Connected through hyperedge HE-8 (induced by source paper "coloring with limited data few shot colorization via memory augmented networks"):
  with members:
    - "a memory network approach for story based temporal summarization of 360 deg videos"
    - "memorization precedes generation learning unsupervised gans with memory networks"
    - "image to image translation via group wise deep whitening and coloring transformation"
    - "abstractive summarization of reddit posts with multi level memory networks"
    - "multimodal unsupervised image to image translation"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "ganimation anatomically aware facial animation from a single image"
    - "coloring with words guiding image colorization through text based palette generation"

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)