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: "scaling laws for neural language models"
  Abstract: We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size, dataset size, and the amount of compute used for training, with some trends spanning more than seven orders of magnitude. Other architectural details such as network width or depth have minimal effects within a wide range. Simple equations govern the dependence of overfitting on model/dataset size and the dependence of training speed on model size. These relationships allow us to determine the optimal allocation of a fixed compute budget. Larger models are significantly more sample-efficient, such that optimally compute-efficient training involves training very large models on a relatively modest amount of data and stopping significantly before convergence.

Connected through hyperedge HE-1 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "are sixteen heads really better than one"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "checkmate breaking the memory wall with optimal tensor rematerialization"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "q bert hessian based ultra low precision quantization of bert"

Connected through hyperedge HE-2 (induced by source paper "learning home crowdsourced training of large neural networks using decentralized mixture of experts"):
  with members:
    - "zero memory optimization towards training a trillion parameter models"
    - "megatron lm training multi billion parameter language models using model parallelism"
    - "natural compression for distributed deep learning"
    - "defending against neural fake news"
    - "large scale learning of general visual representations for transfer"
    - "optimizing network performance for distributed dnn training on gpu clusters imagenet alexnet training in 1 5 minutes"
    - "pipemare asynchronous pipeline parallel dnn training"
    - "the hsic bottleneck deep learning without back propagation"

Connected through hyperedge HE-3 (induced by source paper "document ranking with a pretrained sequence to sequence model"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "multi stage document ranking with bert"
    - "passage re ranking with bert"
    - "document expansion by query prediction"
    - "critically examining the neural hype weak baselines and the additivity of effectiveness gains from neural ranking models"
    - "show your work improved reporting of experimental results"
    - "zero shot text classification with generative language models"
    - "cedr contextualized embeddings for document ranking"

Connected through hyperedge HE-4 (induced by source paper "on a class of markov order estimators based on ppm and other universal codes"):
  with members:
    - "calibration entropy rates and memory in language models"
    - "regular hilberg processes an example of processes with a vanishing entropy rate"
    - "mixing ergodic and nonergodic processes with rapidly growing information between blocks"
    - "estimating the entropy of binary time series methodology some theory and a simulation study"

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)