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: "tackling climate change with machine learning"
  Abstract: Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.

Connected through hyperedge HE-1 (induced by source paper "towards the systematic reporting of the energy and carbon footprints of machine learning"):
  with members:
    - "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"
    - "green ai"
    - "analysis of dawnbench a time to accuracy machine learning performance benchmark"

Connected through hyperedge HE-2 (induced by source paper "highres net recursive fusion for multi frame super resolution of satellite imagery"):
  with members:
    - "learning parallax attention for stereo image super resolution"
    - "frame and feature context video super resolution"
    - "deep learning for image super resolution a survey"
    - "edvr video restoration with enhanced deformable convolutional networks"
    - "learning disentangled representations of satellite image time series"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "mapping informal settlements in developing countries with multi resolution multi spectral data"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-3 (induced by source paper "a review of machine learning applications in wildfire science and management"):
  with members:
    - "distributed wildfire surveillance with autonomous aircraft using deep reinforcement learning"
    - "regularization for deep learning a taxonomy"
    - "a survey on data collection for machine learning a big data ai integration perspective"

Connected through hyperedge HE-4 (induced by source paper "should artificial intelligence governance be centralised design lessons from history"):
  with members:
    - "the role of artificial intelligence in achieving the sustainable development goals"
    - "when will ai exceed human performance evidence from ai experts"

Connected through hyperedge HE-5 (induced by source paper "exploring benefits of transfer learning in neural machine translation"):
  with members:
    - "widening the representation bottleneck in neural machine translation with lexical shortcuts"
    - "revisiting low resource neural machine translation a case study"
    - "effective cross lingual transfer of neural machine translation models without shared vocabularies"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "energy and policy considerations for deep learning in nlp"
    - "investigating backtranslation in neural machine translation"
    - "a survey of domain adaptation for neural machine translation"
    - "characterizing and avoiding negative transfer"

Connected through hyperedge HE-6 (induced by source paper "the ethics of ai ethics an evaluation of guidelines"):
  with members:
    - "principles alone cannot guarantee ethical ai"
    - "principles alone cannot guarantee ethical ai"
    - "from what to how an overview of ai ethics tools methods and research to translate principles into practices"
    - "explaining explanations in ai"
    - "forbidden knowledge in machine learning reflections on the limits of research and publication"
    - "the global landscape of ai ethics guidelines"
    - "the malicious use of artificial intelligence forecasting prevention and mitigation"
    - "on quantifying and understanding the role of ethics in ai research a historical account of flagship conferences and journals"

Connected through hyperedge HE-7 (induced by source paper "data driven approach to encoding and decoding 3 d crystal structures"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "molecular geometry prediction using a deep generative graph neural network"
    - "generating diverse high fidelity images with vq vae 2"
    - "generative models for automatic chemical design"
    - "crystalgan learning to discover crystallographic structures with generative adversarial networks"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-8 (induced by source paper "decay replay mining to predict next process events"):
  with members:
    - "process mining of programmable logic controllers input output event logs"
    - "deep learning a critical appraisal"
    - "memory augmented neural networks for predictive process analytics"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "automated discovery of process models from event logs review and benchmark"
    - "predicting process behaviour using deep learning"
    - "lstm networks for data aware remaining time prediction of business process instances"
    - "the internet of things meets business process management mutual benefits and challenges"

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)