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: "artificial neural networks condensation a strategy to facilitate adaption of machine learning in medical settings by reducing computational burden"
  Abstract: Machine Learning (ML) applications on healthcare can have a great impact on people's lives helping deliver better and timely treatment to those in need. At the same time, medical data is usually big and sparse requiring important computational resources. Although it might not be a problem for wide-adoption of ML tools in developed nations, availability of computational resource can very well be limited in third-world nations. This can prevent the less favored people from benefiting of the advancement in ML applications for healthcare. In this project we explored methods to increase computational efficiency of ML algorithms, in particular Artificial Neural Nets (NN), while not compromising the accuracy of the predicted results. We used in-hospital mortality prediction as our case analysis based on the MIMIC III publicly available dataset. We explored three methods on two different NN architectures. We reduced the size of recurrent neural net (RNN) and dense neural net (DNN) by applying pruning of "unused" neurons. Additionally, we modified the RNN structure by adding a hidden-layer to the LSTM cell allowing to use less recurrent layers for the model. Finally, we implemented quantization on DNN forcing the weights to be 8-bits instead of 32-bits. We found that all our methods increased computational efficiency without compromising accuracy and some of them even achieved higher accuracy than the pre-condensed baseline models.

Connected through hyperedge HE-1 (induced by source paper "two stage federated phenotyping and patient representation learning"):
  with members:
    - "towards federated learning at scale system design"
    - "patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records"
    - "federated learning strategies for improving communication efficiency"
    - "loadaboost loss based adaboost federated machine learning on medical data"
    - "fadl federated autonomous deep learning for distributed electronic health record"
    - "learning patient representations from text"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-2 (induced by source paper "loadaboost loss based adaboost federated machine learning on medical data"):
  with members:
    - "patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records"
    - "two stage federated phenotyping and patient representation learning"
    - "federated learning strategies for improving communication efficiency"
    - "fadl federated autonomous deep learning for distributed electronic health record"
    - "how to backdoor federated learning"
    - "federated learning with non iid data"
    - "federated multi task learning"
    - "practical secure aggregation for federated learning on user held data"

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)