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: "byzantine tolerant machine learning"
  Abstract: The growth of data, the need for scalability and the complexity of models used in modern machine learning calls for distributed implementations. Yet, as of today, distributed machine learning frameworks have largely ignored the possibility of arbitrary (i.e., Byzantine) failures. In this paper, we study the robustness to Byzantine failures at the fundamental level of stochastic gradient descent (SGD), the heart of most machine learning algorithms. Assuming a set of $n$ workers, up to $f$ of them being Byzantine, we ask how robust can SGD be, without limiting the dimension, nor the size of the parameter space. #R##N#We first show that no gradient descent update rule based on a linear combination of the vectors proposed by the workers (i.e, current approaches) tolerates a single Byzantine failure. We then formulate a resilience property of the update rule capturing the basic requirements to guarantee convergence despite $f$ Byzantine workers. We finally propose Krum, an update rule that satisfies the resilience property aforementioned. For a $d$-dimensional learning problem, the time complexity of Krum is $O(n^2 \cdot (d + \log n))$.

Connected through hyperedge HE-1 (induced by source paper "communication efficient and byzantine robust distributed learning"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "robust federated learning in a heterogeneous environment"
    - "vqsgd vector quantized stochastic gradient descent"
    - "communication efficient distributed sgd with sketching"
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "federated learning strategies for improving communication efficiency"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "sparsified sgd with memory"

Connected through hyperedge HE-2 (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"
    - "securing distributed machine learning in high dimensions"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "securing distributed machine learning in high dimensions"):
  with members:
    - "byzantine stochastic gradient descent"
    - "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"
    - "being robust in high dimensions can be practical"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"

Connected through hyperedge HE-4 (induced by source paper "securing distributed gradient descent in high dimensional statistical learning"):
  with members:
    - "byzantine stochastic gradient descent"
    - "federated learning strategies for improving communication efficiency"
    - "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"
    - "being robust in high dimensions can be practical"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "resilience a criterion for learning in the presence of arbitrary outliers"

Connected through hyperedge HE-5 (induced by source paper "the convergence of stochastic gradient descent in asynchronous shared memory"):
  with members:
    - "byrdie byzantine resilient distributed coordinate descent for decentralized learning"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "asynchronous stochastic gradient descent with delay compensation"
    - "deep residual learning for image recognition"
    - "staleness aware async sgd for distributed deep learning"
    - "taming the wild a unified analysis of hogwild style algorithms"
    - "global convergence of stochastic gradient descent for some non convex matrix problems"

Connected through hyperedge HE-6 (induced by source paper "byzantine robust distributed learning towards optimal statistical rates"):
  with members:
    - "communication efficient distributed optimization using an approximate newton type method"
    - "loss minimization and parameter estimation with heavy tails"

Connected through hyperedge HE-7 (induced by source paper "distributed statistical machine learning in adversarial settings byzantine gradient descent"):
  with members:
    - "revisiting distributed synchronous sgd"
    - "agnostic estimation of mean and covariance"
    - "robust estimators in high dimensions without the computational intractability"
    - "geometric median in nearly linear time"
    - "federated optimization distributed optimization beyond the datacenter"

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)