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: "deep learning of representations looking forward"
  Abstract: Deep learning research aims at discovering learning algorithms that discover multiple levels of distributed representations, with higher levels representing more abstract concepts. Although the study of deep learning has already led to impressive theoretical results, learning algorithms and breakthrough experiments, several challenges lie ahead. This paper proposes to examine some of these challenges, centering on the questions of scaling deep learning algorithms to much larger models and datasets, reducing optimization difficulties due to ill-conditioning or local minima, designing more efficient and powerful inference and sampling procedures, and learning to disentangle the factors of variation underlying the observed data. It also proposes a few forward-looking research directions aimed at overcoming these challenges.

Connected through hyperedge HE-1 (induced by source paper "learning to continually learn"):
  with members:
    - "backpropamine training self modifying neural networks with differentiable neuromodulated plasticity"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "meta learning representations for continual learning"
    - "differentiable plasticity training plastic neural networks with backpropagation"
    - "continuous learning in single incremental task scenarios"
    - "rotate your networks better weight consolidation and less catastrophic forgetting"
    - "born to learn the inspiration progress and future of evolved plastic artificial neural networks"
    - "explicit inductive bias for transfer learning with convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "recognizing video events with varying rhythms"):
  with members:
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "deep reinforcement learning for unsupervised video summarization with diversity representativeness reward"
    - "blockdrop dynamic inference paths in residual networks"
    - "skip rnn learning to skip state updates in recurrent neural networks"
    - "quasi recurrent neural networks"
    - "temporal segment networks towards good practices for deep action recognition"
    - "end to end learning of action detection from frame glimpses in videos"
    - "a simple way to initialize recurrent networks of rectified linear units"

Connected through hyperedge HE-3 (induced by source paper "measuring relative opinion from location based social media a case study of the 2016 u s presidential election"):
  with members:
    - "validation of twitter opinion trends with national polling aggregates hillary clinton vs donald trump"
    - "election bias comparing polls and twitter in the 2016 u s election"
    - "scaling laws in geo located twitter data"
    - "using word embeddings in twitter election classification"
    - "distributed representations of sentences and documents"
    - "distributed representations of words and phrases and their compositionality"
    - "a meta analysis of state of the art electoral prediction from twitter data"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-4 (induced by source paper "deep convolutional neural networks for brain image analysis on magnetic resonance imaging a review"):
  with members:
    - "brain tumor segmentation with deep neural networks"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "improving automated multiple sclerosis lesion segmentation with a cascaded 3d convolutional neural network approach"
    - "a survey on deep learning in medical image analysis"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "generalised wasserstein dice score for imbalanced multi class segmentation using holistic convolutional networks"
    - "deepnat deep convolutional neural network for segmenting neuroanatomy"

Connected through hyperedge HE-5 (induced by source paper "a survey of techniques all classifiers can learn from deep networks models optimizations and regularization"):
  with members:
    - "deep learning for sensor based activity recognition a survey"
    - "multi layered gradient boosting decision trees"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "sampling strategies in siamese networks for unsupervised speech representation learning"
    - "deep neural decision trees"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "embedding visual hierarchy with deep networks for large scale visual recognition"

Connected through hyperedge HE-6 (induced by source paper "dynamic convolutions exploiting spatial sparsity for faster inference"):
  with members:
    - "batch shaping for learning conditional channel gated networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "sbnet sparse blocks network for fast inference"
    - "mnasnet platform aware neural architecture search for mobile"
    - "dynamic channel pruning feature boosting and suppression"
    - "skipnet learning dynamic routing in convolutional networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "an analysis of deep neural network models for practical applications"

Connected through hyperedge HE-7 (induced by source paper "pixelvae improved pixelvae with discrete prior"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "gumbolt extending gumbel trick to boltzmann priors"
    - "deep visual domain adaptation a survey"
    - "dvae discrete variational autoencoders with overlapping transformations"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "prototypical networks for few shot learning"
    - "fixing a broken elbo"
    - "vae with a vampprior"

Connected through hyperedge HE-8 (induced by source paper "exploring uncertainty measures for image caption embedding and retrieval task"):
  with members:
    - "a closer look at few shot classification"
    - "exploring uncertainty in conditional multi modal retrieval systems"
    - "quantifying uncertainties in natural language processing tasks"
    - "recipeqa a challenge dataset for multimodal comprehension of cooking recipes"
    - "object hallucination in image captioning"
    - "anomaly machine component detection by deep generative model with unregularized score"
    - "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"

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)