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: "split learning for health distributed deep learning without sharing raw patient data"
  Abstract: Can health entities collaboratively train deep learning models without sharing sensitive raw data? This paper proposes several configurations of a distributed deep learning method called SplitNN to facilitate such collaborations. SplitNN does not share raw data or model details with collaborating institutions. The proposed configurations of splitNN cater to practical settings of i) entities holding different modalities of patient data, ii) centralized and local health entities collaborating on multiple tasks and iii) learning without sharing labels. We compare performance and resource efficiency trade-offs of splitNN and other distributed deep learning methods like federated learning, large batch synchronous stochastic gradient descent and show highly encouraging results for splitNN.

Connected through hyperedge HE-1 (induced by source paper "secure and robust machine learning for healthcare a survey"):
  with members:
    - "securing future autonomous connected vehicles challenges posed by adversarial machine learning and the way forward"
    - "reproducibility in machine learning for health"
    - "fairness in algorithmic decision making an excursion through the lens of causality"
    - "caveat emptor the risks of using big data for human development"
    - "motion corrected multishot mri reconstruction using generative networks with sensitivity encoding"
    - "the adversarial machine learning conundrum can the insecurity of ml become the achilles heel of cognitive networks"
    - "self supervised domain adaptation for computer vision tasks"
    - "feature robustness in non stationary health records caveats to deployable model performance in common clinical machine learning tasks"

Connected through hyperedge HE-2 (induced by source paper "privacy preserving learning via deep net pruning"):
  with members:
    - "deep leakage from gradients"
    - "stronger l2 l2 compressed sensing without iterating"
    - "inverting deep generative models one layer at a time"
    - "nearly sample optimal sparse fourier transform in any dimension ripless and filterless"
    - "the state of sparsity in deep neural networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "federated learning strategies for improving communication efficiency"
    - "sample efficient estimation and recovery in sparse fft via isolation on average"

Connected through hyperedge HE-3 (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"
    - "distributed learning of deep neural network over multiple agents"
    - "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-4 (induced by source paper "privacy preserving deep learning computation for geo distributed medical big data platforms"):
  with members:
    - "revisiting distributed synchronous sgd"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-5 (induced by source paper "orthogonal gradient descent for continual learning"):
  with members:
    - "prototype reminding for continual learning"
    - "uncertainty guided continual learning with bayesian neural networks"
    - "learn to grow a continual structure learning framework for overcoming catastrophic forgetting"
    - "continual lifelong learning with neural networks a review"
    - "towards federated learning at scale system design"
    - "variational prototype replays for continual learning"
    - "attention based structural plasticity"
    - "stochastic mirror descent on overparameterized nonlinear models convergence implicit regularization and generalization"

Connected through hyperedge HE-6 (induced by source paper "maximal adversarial perturbations for obfuscation hiding certain attributes while preserving rest"):
  with members:
    - "distributed generation of privacy preserving data with user customization"
    - "generating user friendly explanations for loan denials using gans"
    - "fair lending needs explainable models for responsible recommendation"
    - "privacy protective gan for face de identification"
    - "an interpretable model with globally consistent explanations for credit risk"
    - "balanced datasets are not enough estimating and mitigating gender bias in deep image representations"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "synthesizing robust adversarial examples"

Connected through hyperedge HE-7 (induced by source paper "reproducibility in machine learning for health"):
  with members:
    - "rethinking clinical prediction why machine learning must consider year of care and feature aggregation"
    - "generalizability of predictive models for intensive care unit patients"
    - "do cifar 10 classifiers generalize to cifar 10"
    - "datasheets for datasets"
    - "scalable and accurate deep learning for electronic health records"
    - "deep reinforcement learning that matters"
    - "on the state of the art of evaluation in neural language models"

Connected through hyperedge HE-8 (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"
    - "no peek a survey of private distributed deep learning"
    - "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"

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)