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 speech scaling up end to end speech recognition"
  Abstract: We present a state-of-the-art speech recognition system developed using end-to-end deep learning. Our architecture is significantly simpler than traditional speech systems, which rely on laboriously engineered processing pipelines; these traditional systems also tend to perform poorly when used in noisy environments. In contrast, our system does not need hand-designed components to model background noise, reverberation, or speaker variation, but instead directly learns a function that is robust to such effects. We do not need a phoneme dictionary, nor even the concept of a "phoneme." Key to our approach is a well-optimized RNN training system that uses multiple GPUs, as well as a set of novel data synthesis techniques that allow us to efficiently obtain a large amount of varied data for training. Our system, called Deep Speech, outperforms previously published results on the widely studied Switchboard Hub5'00, achieving 16.0% error on the full test set. Deep Speech also handles challenging noisy environments better than widely used, state-of-the-art commercial speech systems.

Connected through hyperedge HE-1 (induced by source paper "flexibo cost aware multi objective optimization of deep neural networks"):
  with members:
    - "transfer learning for performance modeling of deep neural network systems"
    - "fixynn efficient hardware for mobile computer vision via transfer learning"
    - "learning software configuration spaces a systematic literature review"
    - "multi objective bayesian optimisation with preferences over objectives"
    - "a flexible framework for multi objective bayesian optimization using random scalarizations"
    - "ordered preference elicitation strategies for supporting multi objective decision making"
    - "towards automated deep learning efficient joint neural architecture and hyperparameter search"
    - "proxylessnas direct neural architecture search on target task and hardware"

Connected through hyperedge HE-2 (induced by source paper "rpn a residual pooling network for efficient federated learning"):
  with members:
    - "robust and communication efficient federated learning from non iid data"
    - "federated machine learning concept and applications"
    - "variance based gradient compression for efficient distributed deep learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "signsgd compressed optimisation for non convex problems"
    - "expanding the reach of federated learning by reducing client resource requirements"
    - "sparse communication for distributed gradient descent"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"

Connected through hyperedge HE-3 (induced by source paper "identifying audio adversarial examples via anomalous pattern detection"):
  with members:
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "robust audio adversarial example for a physical attack"
    - "robust over the air adversarial examples against automatic speech recognition systems"
    - "targeted adversarial examples for black box audio systems"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "adagio interactive experimentation with adversarial attack and defense for audio"
    - "isolated and ensemble audio preprocessing methods for detecting adversarial examples against automatic speech recognition"
    - "characterizing audio adversarial examples using temporal dependency"

Connected through hyperedge HE-4 (induced by source paper "spatial temporal multi cue network for continuous sign language recognition"):
  with members:
    - "deep high resolution representation learning for human pose estimation"
    - "skeleton based gesture recognition using several fully connected layers with path signature features and temporal transformer module"
    - "simple baselines for human pose estimation and tracking"
    - "video based sign language recognition without temporal segmentation"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "convolutional two stream network fusion for video action recognition"
    - "fast r cnn"
    - "sequence to sequence video to text"

Connected through hyperedge HE-5 (induced by source paper "txsim modeling training of deep neural networks on resistive crossbar systems"):
  with members:
    - "training large scale anns on simulated resistive crossbar arrays"
    - "rxnn a framework for evaluating deep neural networks on resistive crossbars"
    - "technology aware training in memristive neuromorphic systems for nonideal synaptic crossbars"
    - "in datacenter performance analysis of a tensor processing unit"
    - "analog cmos based resistive processing unit for deep neural network training"
    - "a survey of neuromorphic computing and neural networks in hardware"
    - "training deep convolutional neural networks with resistive cross point devices"
    - "deep learning with limited numerical precision"

Connected through hyperedge HE-6 (induced by source paper "distributed training of deep neural network acoustic models for automatic speech recognition"):
  with members:
    - "distributed deep learning strategies for automatic speech recognition"
    - "realizing petabyte scale acoustic modeling"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "english conversational telephone speech recognition by humans and machines"
    - "sparse communication for distributed gradient descent"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "unsupervised data augmentation for consistency training"):
  with members:
    - "s4l self supervised semi supervised learning"
    - "stochastic beams and where to find them the gumbel top k trick for sampling sequences without replacement"
    - "semi supervised learning by label gradient alignment"
    - "randaugment practical data augmentation with no separate search"
    - "mixture models for diverse machine translation tricks of the trade"
    - "are labels required for improving adversarial robustness"
    - "adversarially robust generalization just requires more unlabeled data"
    - "data efficient image recognition with contrastive predictive coding"

Connected through hyperedge HE-8 (induced by source paper "local aggregation for unsupervised learning of visual embeddings"):
  with members:
    - "contrastive multiview coding"
    - "data efficient image recognition with contrastive predictive coding"
    - "learning representations by maximizing mutual information across views"
    - "deep clustering for unsupervised learning of visual features"
    - "unsupervised representation learning by predicting image rotations"
    - "improving generalization via scalable neighborhood component analysis"
    - "representation learning with contrastive predictive coding"
    - "self supervised feature learning by learning to spot artifacts"

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)