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: "auto sizing neural networks with applications to n gram language models"
  Abstract: Neural networks have been shown to improve performance across a range of natural-language tasks. However, designing and training them can be complicated. Frequently, researchers resort to repeated experimentation to pick optimal settings. In this paper, we address the issue of choosing the correct number of units in hidden layers. We introduce a method for automatically adjusting network size by pruning out hidden units through $\ell_{\infty,1}$ and $\ell_{2,1}$ regularization. We apply this method to language modeling and demonstrate its ability to correctly choose the number of hidden units while maintaining perplexity. We also include these models in a machine translation decoder and show that these smaller neural models maintain the significant improvements of their unpruned versions.

Connected through hyperedge HE-1 (induced by source paper "reducing transformer depth on demand with structured dropout"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "are sixteen heads really better than one"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "adaptively sparse transformers"
    - "very deep self attention networks for end to end speech recognition"
    - "pay less attention with lightweight and dynamic convolutions"
    - "learning deep transformer models for machine translation"
    - "pre trained language model representations for language generation"

Connected through hyperedge HE-2 (induced by source paper "deep sparse band selection for hyperspectral face recognition"):
  with members:
    - "using deep cross modal hashing and error correcting codes for improving the efficiency of attribute guided facial image retrieval"
    - "attribute guided coupled gan for cross resolution face recognition"
    - "adversarial examples to fool iris recognition systems"
    - "defending against adversarial iris examples using wavelet decomposition"
    - "unsupervised image to image translation using domain specific variational information bound"
    - "unsupervised facial geometry learning for sketch to photo synthesis"
    - "object detection with deep learning a review"
    - "multibiometric secure system based on deep learning"

Connected through hyperedge HE-3 (induced by source paper "are sixteen heads really better than one"):
  with members:
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "transformer xl attentive language models beyond a fixed length context"
    - "weighted transformer network for machine translation"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "scaling neural machine translation"
    - "neural network acceptability judgments"
    - "mtnt a testbed for machine translation of noisy text"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "in training matrix factorization for parameter frugal neural machine translation"):
  with members:
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "training tips for the transformer model"
    - "pieces of eight 8 bit neural machine translation"
    - "attention is all you need"
    - "sharp models on dull hardware fast and accurate neural machine translation decoding on the cpu"
    - "opennmt open source toolkit for neural machine translation"
    - "unfolding and shrinking neural machine translation ensembles"
    - "factorization tricks for lstm networks"

Connected through hyperedge HE-5 (induced by source paper "refining the structure of neural networks using matrix conditioning"):
  with members:
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "efficient forward architecture search"
    - "practical deep learning architecture optimization"
    - "the singular values of convolutional layers"
    - "trained rank pruning for efficient deep neural networks"
    - "efficient neural architecture search via parameter sharing"
    - "progressive neural architecture search"
    - "an analysis of deep neural network models for practical applications"

Connected through hyperedge HE-6 (induced by source paper "auto sizing the transformer network improving speed efficiency and performance for low resource machine translation"):
  with members:
    - "random search and reproducibility for neural architecture search"
    - "recombination of artificial neural networks"
    - "energy and policy considerations for deep learning in nlp"
    - "training tips for the transformer model"
    - "attention is all you need"
    - "shadho massively scalable hardware aware distributed hyperparameter optimization"
    - "massive exploration of neural machine translation architectures"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-7 (induced by source paper "efficiency through auto sizing notre dame nlp s submission to the wngt 2019 efficiency task"):
  with members:
    - "are sixteen heads really better than one"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "findings of the third workshop on neural generation and translation"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "learning the number of neurons in deep networks"
    - "linguistic input features improve neural machine translation"
    - "compression of neural machine translation models via pruning"

Connected through hyperedge HE-8 (induced by source paper "morphnet fast simple resource constrained structure learning of deep networks"):
  with members:
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "learning efficient convolutional networks through network slimming"
    - "data driven sparse structure selection for deep neural networks"
    - "large scale evolution of image classifiers"
    - "learning transferable architectures for scalable image recognition"
    - "learning time efficient deep architectures with budgeted super networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "learning structured sparsity in 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.CL(Computation and Language)