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: "privynet a flexible framework for privacy preserving deep neural network training"
  Abstract: Massive data exist among user local platforms that usually cannot support deep neural network (DNN) training due to computation and storage resource constraints. Cloud-based training schemes provide beneficial services but suffer from potential privacy risks due to excessive user data collection. To enable cloud-based DNN training while protecting the data privacy simultaneously, we propose to leverage the intermediate representations of the data, which is achieved by splitting the DNNs and deploying them separately onto local platforms and the cloud. The local neural network (NN) is used to generate the feature representations. To avoid local training and protect data privacy, the local NN is derived from pre-trained NNs. The cloud NN is then trained based on the extracted intermediate representations for the target learning task. We validate the idea of DNN splitting by characterizing the dependency of privacy loss and classification accuracy on the local NN topology for a convolutional NN (CNN) based image classification task. Based on the characterization, we further propose PrivyNet to determine the local NN topology, which optimizes the accuracy of the target learning task under the constraints on privacy loss, local computation, and storage. The efficiency and effectiveness of PrivyNet are demonstrated with CIFAR-10 dataset.

Connected through hyperedge HE-1 (induced by source paper "deep quaternion features for privacy protection"):
  with members:
    - "adversarial neural network inversion via auxiliary knowledge alignment"
    - "complex valued neural networks for privacy protection"
    - "interpretable complex valued neural networks for privacy protection"
    - "quaternion convolutional neural networks"
    - "exploiting unintended feature leakage in collaborative learning"
    - "gazelle a low latency framework for secure neural network inference"
    - "not just privacy improving performance of private deep learning in mobile cloud"
    - "deep complex networks"

Connected through hyperedge HE-2 (induced by source paper "overlearning reveals sensitive attributes"):
  with members:
    - "similarity of neural network representations revisited"
    - "privacy partitioning protecting user data during the deep learning inference phase"
    - "exploiting unintended feature leakage in collaborative learning"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "isolating sources of disentanglement in variational autoencoders"
    - "privacy preserving neural representations of text"
    - "adversarial removal of demographic attributes from text data"
    - "learning adversarially fair and transferable representations"

Connected through hyperedge HE-3 (induced by source paper "interpretable complex valued neural networks for privacy protection"):
  with members:
    - "3d rotation equivariant quaternion neural networks"
    - "exploiting unintended feature leakage in collaborative learning"
    - "not just privacy improving performance of private deep learning in mobile cloud"
    - "associative long short term memory"
    - "membership inference attacks against machine learning models"
    - "inverting visual representations with convolutional networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "securing input data of deep learning inference systems via partitioned enclave execution"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "chiron privacy preserving machine learning as a service"
    - "mlcapsule guarded offline deployment of machine learning as a service"
    - "efficient deep learning on multi source private data"
    - "the circle game scalable private membership test using trusted hardware"
    - "opening the black box of deep neural networks via information"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "software grand exposure sgx cache attacks are practical"

Connected through hyperedge HE-5 (induced by source paper "deep learning towards mobile applications"):
  with members:
    - "not just privacy improving performance of private deep learning in mobile cloud"
    - "sequential keystroke behavioral biometrics for mobile user identification via multi view deep learning"
    - "deepmood modeling mobile phone typing dynamics for mood detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "learning differentially private recurrent language models"
    - "modeling source syntax for neural machine translation"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"

Connected through hyperedge HE-6 (induced by source paper "not just privacy improving performance of private deep learning in mobile cloud"):
  with members:
    - "sequential keystroke behavioral biometrics for mobile user identification via multi view deep learning"
    - "adversarial dropout for supervised and semi supervised learning"
    - "deepmood modeling mobile phone typing dynamics for mood detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "modeling source syntax for neural machine translation"
    - "distributed deep neural networks over the cloud the edge and end devices"
    - "c ir cnn accelerating and compressing deep neural networks using block circulant weight matrices"
    - "a hybrid deep learning architecture for privacy preserving mobile analytics"

Connected through hyperedge HE-7 (induced by source paper "privacy preserving collaborative deep learning with irregular participants"):
  with members:
    - "xonn xnor based oblivious deep neural network inference"
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "deep learning based gait recognition using smartphones in the wild"
    - "differentially private federated learning a client level perspective"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "stealing machine learning models via prediction apis"
    - "membership inference attacks against machine learning models"
    - "robust gait recognition by integrating inertial and rgbd sensors"

Connected through hyperedge HE-8 (induced by source paper "privacy preserving collaborative deep learning with unreliable participants"):
  with members:
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "deep learning based gait recognition using smartphones in the wild"
    - "differentially private federated learning a client level perspective"
    - "deep models under the gan information leakage from collaborative deep learning"
    - "stealing machine learning models via prediction apis"
    - "membership inference attacks against machine learning models"
    - "deep learning in neural networks"
    - "deep residual learning for image recognition"

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)