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: "securing distributed machine learning in high dimensions"
  Abstract: We consider securing a distributed machine learning system wherein the data is kept confidential by its providers who are recruited as workers to help the learner to train a $d$--dimensional model. In each communication round, up to $q$ out of the $m$ workers suffer Byzantine faults; faulty workers are assumed to have complete knowledge of the system and can collude to behave arbitrarily adversarially against the learner. We assume that each worker keeps a local sample of size $n$. (Thus, the total number of data points is $N=nm$.) Of particular interest is the high-dimensional regime $d \gg n$. #R##N#We propose a secured variant of the classical gradient descent method which can tolerate up to a constant fraction of Byzantine workers. We show that the estimation error of the iterates converges to an estimation error $O(\sqrt{q/N} + \sqrt{d/N})$ in $O(\log N)$ rounds. The core of our method is a robust gradient aggregator based on the iterative filtering algorithm proposed by Steinhardt et al. \cite{Steinhardt18} for robust mean estimation. We establish a uniform concentration of the sample covariance matrix of gradients, and show that the aggregated gradient, as a function of model parameter, converges uniformly to the true gradient function. As a by-product, we develop a new concentration inequality for sample covariance matrices of sub-exponential distributions, which might be of independent interest.

Connected through hyperedge HE-1 (induced by source paper "threats to federated learning a survey"):
  with members:
    - "fedvision an online visual object detection platform powered by federated learning"
    - "idlg improved deep leakage from gradients"
    - "advances and open problems in federated learning"
    - "deep leakage from gradients"
    - "towards fair and decentralized privacy preserving deep learning with blockchain"
    - "federated learning challenges methods and future directions"
    - "federated machine learning concept and applications"
    - "analyzing federated learning through an adversarial lens"

Connected through hyperedge HE-2 (induced by source paper "federated machine learning concept and applications"):
  with members:
    - "on device federated learning via blockchain and its latency analysis"
    - "exploiting unintended feature leakage in collaborative learning"
    - "federated learning strategies for improving communication efficiency"
    - "entity resolution and federated learning get a federated resolution"
    - "how to backdoor federated learning"
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "federated meta learning for recommendation"
    - "federated learning with non iid data"

Connected through hyperedge HE-3 (induced by source paper "high dimensional robust m estimation arbitrary corruption and heavy tails"):
  with members:
    - "high dimensional robust sparse regression"
    - "compressed sensing with adversarial sparse noise via l1 regression"
    - "learning with bad training data via iterative trimmed loss minimization"
    - "iteratively learning from the best"
    - "efficient algorithms for outlier robust regression"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "sever a robust meta algorithm for stochastic optimization"
    - "defending against saddle point attack in byzantine robust distributed learning"

Connected through hyperedge HE-4 (induced by source paper "federated variance reduced stochastic gradient descent with robustness to byzantine attacks"):
  with members:
    - "fall of empires breaking byzantine tolerant sgd by inner product manipulation"
    - "detox a redundancy based framework for faster and more robust gradient aggregation"
    - "rsa byzantine robust stochastic aggregation methods for distributed learning from heterogeneous datasets"
    - "generalized byzantine tolerant sgd"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "defending against saddle point attack in byzantine robust distributed learning"
    - "the internet of things secure distributed inference"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"

Connected through hyperedge HE-5 (induced by source paper "defending against saddle point attack in byzantine robust distributed learning"):
  with members:
    - "byzantine stochastic gradient descent"
    - "high dimensional robust sparse regression"
    - "generalized byzantine tolerant sgd"
    - "efficient algorithms for outlier robust regression"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "sever a robust meta algorithm for stochastic optimization"
    - "being robust in high dimensions can be practical"
    - "neon2 finding local minima via first order oracles"

Connected through hyperedge HE-6 (induced by source paper "rsa byzantine robust stochastic aggregation methods for distributed learning from heterogeneous datasets"):
  with members:
    - "byzantine stochastic gradient descent"
    - "zeno byzantine suspicious stochastic gradient descent"
    - "generalized byzantine tolerant sgd"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "phocas dimensional byzantine resilient stochastic gradient descent"
    - "defending against saddle point attack in byzantine robust distributed learning"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "federated multi task learning"

Connected through hyperedge HE-7 (induced by source paper "byrdie byzantine resilient distributed coordinate descent for decentralized learning"):
  with members:
    - "byzantine stochastic gradient descent"
    - "generalized byzantine tolerant sgd"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "defending against saddle point attack in byzantine robust distributed learning"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "cloud k svd a collaborative dictionary learning algorithm for big distributed data"
    - "fault tolerant distributed optimization part iv constrained optimization with arbitrary directed networks"
    - "byzantine multi agent optimization part ii"

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.DC(Distributed, Parallel, and Cluster Computing)