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: "beyond human level accuracy computational challenges in deep learning"
  Abstract: Deep learning (DL) research yields accuracy and product improvements from both model architecture changes and scale: larger data sets and models, and more computation. For hardware design, it is difficult to predict DL model changes. However, recent prior work shows that as dataset sizes grow, DL model accuracy and model size grow predictably. This paper leverages the prior work to project the dataset and model size growth required to advance DL accuracy beyond human-level, to frontier targets defined by machine learning experts. Datasets will need to grow $33$--$971 \times$, while models will need to grow $6.6$--$456\times$ to achieve target accuracies. #R##N#We further characterize and project the computational requirements to train these applications at scale. Our characterization reveals an important segmentation of DL training challenges for recurrent neural networks (RNNs) that contrasts with prior studies of deep convolutional networks. RNNs will have comparatively moderate operational intensities and very large memory footprint requirements. In contrast to emerging accelerator designs, large-scale RNN training characteristics suggest designs with significantly larger memory capacity and on-chip caches.

Connected through hyperedge HE-1 (induced by source paper "scaling laws for neural language models"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "which algorithmic choices matter at which batch sizes insights from a noisy quadratic model"
    - "growing a brain fine tuning by increasing model capacity"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "autogrow automatic layer growing in deep convolutional networks"
    - "a constructive prediction of the generalization error across scales"
    - "universal transformers"
    - "generating long sequences with sparse transformers"

Connected through hyperedge HE-2 (induced by source paper "social and governance implications of improved data efficiency"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "the offense defense balance of scientific knowledge does publishing ai research reduce misuse"
    - "few shot adversarial learning of realistic neural talking head models"
    - "data efficient image recognition with contrastive predictive coding"
    - "impact of training dataset size on neural answer selection models"
    - "the malicious use of artificial intelligence forecasting prevention and mitigation"
    - "soft actor critic algorithms and applications"
    - "scalable agent alignment via reward modeling a research direction"

Connected through hyperedge HE-3 (induced by source paper "distributed mean estimation with optimal error bounds"):
  with members:
    - "nuqsgd improved communication efficiency for data parallel sgd via nonuniform quantization"
    - "vqsgd vector quantized stochastic gradient descent"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "terngrad ternary gradients to reduce communication in distributed deep learning"
    - "qsgd communication efficient sgd via gradient quantization and encoding"
    - "tensorflow a system for large scale machine learning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "prema a predictive multi task scheduling algorithm for preemptible neural processing units"):
  with members:
    - "tensordimm a practical near memory processing architecture for embeddings and tensor operations in deep learning"
    - "beyond the memory wall a case for memory centric hpc system for deep learning"
    - "scale sim systolic cnn accelerator simulator"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "in datacenter performance analysis of a tensor processing unit"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "massive exploration of neural machine translation architectures"

Connected through hyperedge HE-5 (induced by source paper "neummu architectural support for efficient address translations in neural processing units"):
  with members:
    - "deep learning recommendation model for personalization and recommendation systems"
    - "prema a predictive multi task scheduling algorithm for preemptible neural processing units"
    - "tensordimm a practical near memory processing architecture for embeddings and tensor operations in deep learning"
    - "beyond the memory wall a case for memory centric hpc system for deep learning"
    - "bit tactical exploiting ineffectual computations in convolutional neural networks which why and how"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "in datacenter performance analysis of a tensor processing unit"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "tensordimm a practical near memory processing architecture for embeddings and tensor operations in deep learning"):
  with members:
    - "the architectural implications of facebook s dnn based personalized recommendation"
    - "beyond the memory wall a case for memory centric hpc system for deep learning"
    - "buddy compression enabling larger memory for deep learning and hpc workloads on gpus"
    - "bit tactical exploiting ineffectual computations in convolutional neural networks which why and how"
    - "competitive analysis system for theatrical movie releases based on movie trailer deep video representation"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "in datacenter performance analysis of a tensor processing unit"
    - "neural collaborative filtering"

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)