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: "no peek a survey of private distributed deep learning"
  Abstract: We survey distributed deep learning models for training or inference without accessing raw data from clients. These methods aim to protect confidential patterns in data while still allowing servers to train models. The distributed deep learning methods of federated learning, split learning and large batch stochastic gradient descent are compared in addition to private and secure approaches of differential privacy, homomorphic encryption, oblivious transfer and garbled circuits in the context of neural networks. We study their benefits, limitations and trade-offs with regards to computational resources, data leakage and communication efficiency and also share our anticipated future trends.

Connected through hyperedge HE-1 (induced by source paper "a survey on distributed machine learning"):
  with members:
    - "mxnet mpi embedding mpi parallelism in parameter server task model for scaling deep learning"
    - "how to backdoor federated learning"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"
    - "scaling deep learning on gpu and knights landing clusters"
    - "revisiting distributed synchronous sgd"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "deep models under the gan information leakage from collaborative deep learning"

Connected through hyperedge HE-2 (induced by source paper "distilling on device intelligence at the network edge"):
  with members:
    - "gadmm fast and communication efficient framework for distributed machine learning"
    - "federated reinforcement distillation with proxy experience memory"
    - "multi hop federated private data augmentation with sample compression"
    - "robust coreset construction for distributed machine learning"
    - "federated machine learning concept and applications"
    - "wireless network intelligence at the edge"
    - "large scale distributed neural network training through online distillation"
    - "adaptive federated learning in resource constrained edge computing systems"

Connected through hyperedge HE-3 (induced by source paper "split learning for collaborative deep learning in healthcare"):
  with members:
    - "expertmatcher automating ml model selection for clients using hidden representations"
    - "detailed comparison of communication efficiency of split learning and federated learning"
    - "distributed learning of deep neural network over multiple agents"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "revisiting distributed synchronous sgd"
    - "federated learning of deep networks using model averaging"
    - "experiments on parallel training of deep neural network using model averaging"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "expertmatcher automating ml model selection for clients using hidden representations"):
  with members:
    - "expertmatcher automating ml model selection for users in resource constrained countries"
    - "split learning for health distributed deep learning without sharing raw patient data"
    - "flash photography for data driven hidden scene recovery"
    - "network of experts for large scale image categorization"
    - "expert gate lifelong learning with a network of experts"

Connected through hyperedge HE-5 (induced by source paper "expertmatcher automating ml model selection for users in resource constrained countries"):
  with members:
    - "split learning for health distributed deep learning without sharing raw patient data"
    - "variable generalization performance of a deep learning model to detect pneumonia in chest radiographs a cross sectional study"
    - "flash photography for data driven hidden scene recovery"
    - "network of experts for large scale image categorization"
    - "expert gate lifelong learning with a network of experts"

Connected through hyperedge HE-6 (induced by source paper "data markets to support ai for all pricing valuation and governance"):
  with members:
    - "towards efficient data valuation based on the shapley value"
    - "split learning for health distributed deep learning without sharing raw patient data"
    - "distributed learning of deep neural network over multiple agents"
    - "a theory of pricing private 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)