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: "privacy preserving personal model training"
  Abstract: Many current Internet services rely on inferences from models trained on user data. Commonly, both the training and inference tasks are carried out using cloud resources fed by personal data collected at scale from users. Holding and using such large collections of personal data in the cloud creates privacy risks to the data subjects, but is currently required for users to benefit from such services. We explore how to provide for model training and inference in a system where computation is pushed to the data in preference to moving data to the cloud, obviating many current privacy risks. Specifically, we take an initial model learnt from a small set of users and retrain it locally using data from a single user. We evaluate on two tasks: one supervised learning task, using a neural network to recognise users' current activity from accelerometer traces; and one unsupervised learning task, identifying topics in a large set of documents. In both cases the accuracy is improved. We also analyse the robustness of our approach against adversarial attacks, as well as its feasibility by presenting a performance evaluation on a representative resource-constrained device (a Raspberry Pi).

Connected through hyperedge HE-1 (induced by source paper "optimizing deep learning inference on embedded systems through adaptive model selection"):
  with members:
    - "characterizing scalability of sparse matrix vector multiplications on phytium ft 2000"
    - "transferable knowledge for low cost decision making in cloud environments"
    - "rectified decision trees towards interpretability compression and empirical soundness"
    - "the university of cambridge s machine translation systems for wmt18"
    - "multi representation ensembles and delayed sgd updates improve syntax based nmt"
    - "an analysis of deep neural network models for practical applications"
    - "fake it till you make it fishing for catfishes"
    - "quantization and training of neural networks for efficient integer arithmetic only inference"

Connected through hyperedge HE-2 (induced by source paper "mobile sensor data anonymization"):
  with members:
    - "replacement autoencoder a privacy preserving algorithm for sensory data analysis"
    - "optimal utility privacy trade off with the total variation distance as the privacy measure"
    - "distributed one class learning"
    - "protecting sensory data against sensitive inferences"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "privacy preserving adversarial networks"
    - "context aware generative adversarial privacy"
    - "deeprotect enabling inference based access control on mobile sensing applications"

Connected through hyperedge HE-3 (induced by source paper "challenges of privacy preserving machine learning in iot"):
  with members:
    - "on lightweight privacy preserving collaborative learning for internet of things objects"
    - "analyzing federated learning through an adversarial lens"
    - "how to backdoor federated learning"
    - "differentially private mixture of generative neural networks"
    - "context aware generative adversarial privacy"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "communication efficient learning of deep networks from decentralized data"
    - "improving the robustness of deep neural networks via stability training"

Connected through hyperedge HE-4 (induced by source paper "exploring the effectiveness of service decomposition in fog computing architecture for the internet of things"):
  with members:
    - "fog computing for sustainable smart cities a survey"
    - "enorm a framework for edge node resource management"
    - "microservices granularity vs performance"
    - "a hybrid approach for data analytics for internet of things"
    - "valorising the iot databox creating value for everyone"
    - "a survey on internet of things from industrial market perspective"

Connected through hyperedge HE-5 (induced by source paper "data analytics service composition and deployment on edge devices"):
  with members:
    - "owl a general purpose numerical library in ocaml"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deep photo style transfer"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "noscope optimizing neural network queries over video at scale"
    - "clipper a low latency online prediction serving system"
    - "end to end learning for self driving cars"
    - "compressing neural networks with the hashing trick"

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)