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: "green ai"
  Abstract: The computations required for deep learning research have been doubling every few months, resulting in an estimated 300,000x increase from 2012 to 2018 [2]. These computations have a surprisingly large carbon footprint [38]. Ironically, deep learning was inspired by the human brain, which is remarkably energy efficient. Moreover, the financial cost of the computations can make it difficult for academics, students, and researchers, in particular those from emerging economies, to engage in deep learning research.  This position paper advocates a practical solution by making efficiency an evaluation criterion for research alongside accuracy and related measures. In addition, we propose reporting the financial cost or "price tag" of developing, training, and running models to provide baselines for the investigation of increasingly efficient methods. Our goal is to make AI both greener and more inclusive---enabling any inspired undergraduate with a laptop to write high-quality research papers. Green AI is an emerging focus at the Allen Institute for AI.

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:
    - "scaling laws for neural language models"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "towards the systematic reporting of the energy and carbon footprints of machine learning"):
  with members:
    - "tackling climate change with machine learning"
    - "pay less attention with lightweight and dynamic convolutions"
    - "accelerating deep learning by focusing on the biggest losers"
    - "hardnet a low memory traffic network"
    - "energy and policy considerations for deep learning in nlp"
    - "quantifying the carbon emissions of machine learning"
    - "single headed attention rnn stop thinking with your head"
    - "analysis of dawnbench a time to accuracy machine learning performance benchmark"

Connected through hyperedge HE-3 (induced by source paper "soft threshold weight reparameterization for learnable sparsity"):
  with members:
    - "rigging the lottery making all tickets winners"
    - "sparse networks from scratch faster training without losing performance"
    - "discovering neural wirings"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "fast sparse convnets"
    - "parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization"
    - "butterfly transform an efficient fft based neural architecture design"
    - "pytorch an imperative style high performance deep learning library"

Connected through hyperedge HE-4 (induced by source paper "understanding and optimizing packed neural network training for hyper parameter tuning"):
  with members:
    - "hypersched dynamic resource reallocation for model development on a deadline"
    - "crossbow scaling deep learning with small batch sizes on multi gpu servers"
    - "3lc lightweight and effective traffic compression for distributed machine learning"
    - "efficient memory management for gpu based deep learning systems"
    - "themis fair and efficient gpu cluster scheduling for machine learning workloads"
    - "exploiting reuse in pipeline aware hyperparameter tuning"
    - "analysis of large scale multi tenant gpu clusters for dnn training workloads"
    - "energy and policy considerations for deep learning in nlp"

Connected through hyperedge HE-5 (induced by source paper "hulk an energy efficiency benchmark platform for responsible natural language processing"):
  with members:
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "show your work improved reporting of experimental results"
    - "albert a lite bert for self supervised learning of language representations"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "mlperf training benchmark"
    - "energy and policy considerations for deep learning in nlp"
    - "superglue a stickier benchmark for general purpose language understanding systems"
    - "roberta a robustly optimized bert pretraining approach"

Connected through hyperedge HE-6 (induced by source paper "exploring bert parameter efficiency on the stanford question answering dataset v2 0"):
  with members:
    - "albert a lite bert for self supervised learning of language representations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "learning context sensitive convolutional filters for text processing"
    - "bidirectional attention flow for machine comprehension"

Connected through hyperedge HE-7 (induced by source paper "energy reliability limits in nanoscale feedforward neural networks and formulas"):
  with members:
    - "energy reliability limits in nanoscale circuits"
    - "energy efficient machine learning in silicon a communications inspired approach"

Connected through hyperedge HE-8 (induced by source paper "convert efficient and accurate conversational representations from transformers"):
  with members:
    - "deep retrieval based dialogue systems a short review"
    - "q8bert quantized 8bit bert"
    - "designing for health chatbots"
    - "are sixteen heads really better than one"
    - "dialog system technology challenge 7"
    - "distilbert a distilled version of bert smaller faster cheaper and lighter"
    - "multiwoz 2 1 multi domain dialogue state corrections and state tracking baselines"
    - "extreme language model compression with optimal subwords and shared projections"

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.CY(Computers and Society)