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 networks with large output spaces"
  Abstract: Deep neural networks have been extremely successful at various image, speech, video recognition tasks because of their ability to model deep structures within the data. However, they are still prohibitively expensive to train and apply for problems containing millions of classes in the output layer. Based on the observation that the key computation common to most neural network layers is a vector/matrix product, we propose a fast locality-sensitive hashing technique to approximate the actual dot product enabling us to scale up the training and inference to millions of output classes. We evaluate our technique on three diverse large-scale recognition tasks and show that our approach can train large-scale models at a faster rate (in terms of steps/total time) compared to baseline methods.

Connected through hyperedge HE-1 (induced by source paper "large memory layers with product keys"):
  with members:
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "transformer xl attentive language models beyond a fixed length context"
    - "cross lingual language model pretraining"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "exploring the limits of weakly supervised pretraining"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adaptive input representations for neural language modeling"

Connected through hyperedge HE-2 (induced by source paper "compressing gradient optimizers via count sketches"):
  with members:
    - "large scale language modeling converging on 40gb of text in four hours"
    - "extreme classification in log memory"
    - "scaling neural machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "hash embeddings for efficient word representations"
    - "breaking the softmax bottleneck a high rank rnn language model"

Connected through hyperedge HE-3 (induced by source paper "sampled softmax with random fourier features"):
  with members:
    - "stochastic negative mining for learning with large output spaces"
    - "normface l 2 hypersphere embedding for face verification"
    - "sphereface deep hypersphere embedding for face recognition"
    - "l2 constrained softmax loss for discriminative face verification"
    - "fast amortized inference and learning in log linear models with randomly perturbed nearest neighbor search"
    - "adaptive sampled softmax with kernel based sampling"
    - "efficient softmax approximation for gpus"
    - "ds mlr exploiting double separability for scaling up distributed multinomial logistic regression"

Connected through hyperedge HE-4 (induced by source paper "beyond one hot encoding lower dimensional target embedding"):
  with members:
    - "label embedding for image classification"
    - "learning discriminative features via label consistent neural network"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "hd cnn hierarchical deep convolutional neural network for large scale visual recognition"
    - "imagenet large scale visual recognition challenge"
    - "caffe convolutional architecture for fast feature embedding"
    - "deep learning face attributes in the wild"
    - "microsoft coco common objects in context"

Connected through hyperedge HE-5 (induced by source paper "extreme classification in log memory"):
  with members:
    - "log time and log space extreme classification"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "compressing neural networks with the hashing trick"
    - "logarithmic time online multiclass prediction"
    - "distributed representations of words and phrases and their compositionality"
    - "large scale multi label learning with missing labels"
    - "feature hashing for large scale multitask learning"

Connected through hyperedge HE-6 (induced by source paper "a no regret generalization of hierarchical softmax to extreme multi label classification"):
  with members:
    - "logarithmic time online multiclass prediction"
    - "fast label embeddings via randomized linear algebra"
    - "conditional probability tree estimation analysis and algorithms"
    - "distributed representations of words and phrases and their compositionality"
    - "surrogate regret bounds for bipartite ranking via strongly proper losses"
    - "optimizing f measure a tale of two approaches"
    - "error correcting tournaments"

Connected through hyperedge HE-7 (induced by source paper "improving negative sampling for word representation using self embedded features"):
  with members:
    - "how to evaluate word embeddings on importance of data efficiency and simple supervised tasks"
    - "fast matrix factorization for online recommendation with implicit feedback"
    - "matrix factorization using window sampling and negative sampling for improved word representations"
    - "dependency recurrent neural language models for sentence completion"
    - "neural programmer inducing latent programs with gradient descent"
    - "wordrank learning word embeddings via robust ranking"
    - "character aware neural language models"
    - "a deep architecture for semantic matching with multiple positional sentence representations"

Connected through hyperedge HE-8 (induced by source paper "fast amortized inference and learning in log linear models with randomly perturbed nearest neighbor search"):
  with members:
    - "enriching word vectors with subword information"
    - "clustering is efficient for approximate maximum inner product search"
    - "learning visual features from large weakly supervised data"
    - "imagenet large scale visual recognition challenge"
    - "distributed representations of words and phrases and their compositionality"
    - "on sampling from the gibbs distribution with random maximum a posteriori perturbations"

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.NE(Neural and Evolutionary Computing)