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: "distributed learning of deep neural network over multiple agents"
  Abstract: In domains such as health care and finance, shortage of labeled data and computational resources is a critical issue while developing machine learning algorithms. To address the issue of labeled data scarcity in training and deployment of neural network-based systems, we propose a new technique to train deep neural networks over several data sources. Our method allows for deep neural networks to be trained using data from multiple entities in a distributed fashion. We evaluate our algorithm on existing datasets and show that it obtains performance which is similar to a regular neural network trained on a single machine. We further extend it to incorporate semi-supervised learning when training with few labeled samples, and analyze any security concerns that may arise. Our algorithm paves the way for distributed training of deep neural networks in data sensitive applications when raw data may not be shared directly.

Connected through hyperedge HE-1 (induced by source paper "industrial scale privacy preserving deep neural network"):
  with members:
    - "privacy preserving pca for multiparty modeling"
    - "privacy preserving point of interest recommendation using decentralized matrix factorization"
    - "secure social recommendation based on secret sharing"
    - "practical privacy preserving poi recommendation"
    - "split learning for health distributed deep learning without sharing raw patient data"
    - "securing input data of deep learning inference systems via partitioned enclave execution"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption"

Connected through hyperedge HE-2 (induced by source paper "communication efficient multimodal split learning for mmwave received power prediction"):
  with members:
    - "proactive received power prediction using machine learning and depth images for mmwave networks"
    - "an mdp model for optimal handover decisions in mmwave cellular networks"
    - "a three state received signal strength model for device free localization"

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"
    - "no peek a survey of private distributed deep learning"
    - "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 "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"
    - "no peek a survey of private distributed deep learning"
    - "a theory of pricing private data"

Connected through hyperedge HE-5 (induced by source paper "detailed comparison of communication efficiency of split learning and federated learning"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "split learning for health distributed deep learning without sharing raw patient data"
    - "federated learning of deep networks using model averaging"

Connected through hyperedge HE-6 (induced by source paper "federated and differentially private learning for electronic health records"):
  with members:
    - "renyi differential privacy"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "big data intelligence using distributed deep neural networks"):
  with members:
    - "emnist an extension of mnist to handwritten letters"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-8 (induced by source paper "split learning for health distributed deep learning without sharing raw patient data"):
  with members:
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "federated learning strategies for improving communication efficiency"
    - "efficient deep learning on multi source private data"
    - "mitigating sybils in federated learning poisoning"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "differentially private federated learning a client level perspective"
    - "cryptodl deep neural networks over encrypted data"
    - "revisiting distributed synchronous sgd"

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)