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: "defending against saddle point attack in byzantine robust distributed learning"
  Abstract: In this paper, we study robust large-scale distributed learning in the presence of saddle points in non-convex loss functions. We consider the Byzantine setting where some worker machines may have abnormal or even arbitrary and adversarial behavior. We argue that in the Byzantine setting, optimizing a non-convex function and escaping saddle points become much more challenging, even when robust gradient estimators are used. We develop ByzantinePGD, a robust and communication-efficient algorithm that can provably escape saddle points and converge to approximate local minimizers. The iteration complexity of our algorithm in the Byzantine setting matches that of standard gradient descent in the usual setting. We further provide three robust aggregation subroutines that can be used in ByzantinePGD, including median, trimmed mean, and iterative filtering. We characterize their performance in statistical settings, and argue for their near-optimality in different regimes including the high dimensional setting.

Connected through hyperedge HE-1 (induced by source paper "on the sample complexity of adversarial multi source pac learning"):
  with members:
    - "optimal robust learning of discrete distributions from batches"
    - "byzantine stochastic gradient descent"
    - "do outliers ruin collaboration"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "mitigating sybils in federated learning poisoning"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "on fundamental limits of robust learning"

Connected through hyperedge HE-2 (induced by source paper "robust federated learning in a heterogeneous environment"):
  with members:
    - "robust and communication efficient federated learning from non iid data"
    - "byzantine stochastic gradient descent"
    - "rsa byzantine robust stochastic aggregation methods for distributed learning from heterogeneous datasets"
    - "generalized byzantine tolerant sgd"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "federated learning with non iid data"
    - "learning mixtures of sparse linear regressions using sparse graph codes"
    - "being robust in high dimensions can be practical"

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"
    - "securing distributed machine learning in high dimensions"
    - "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"

Connected through hyperedge HE-4 (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-5 (induced by source paper "detox a redundancy based framework for faster and more robust gradient aggregation"):
  with members:
    - "fall of empires breaking byzantine tolerant sgd by inner product manipulation"
    - "fast and secure distributed learning in high dimension"
    - "sgd decentralized byzantine resilience"
    - "a little is enough circumventing defenses for distributed learning"
    - "byzantine stochastic gradient descent"
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "zeno byzantine suspicious stochastic gradient descent"
    - "generalized byzantine tolerant sgd"

Connected through hyperedge HE-6 (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"
    - "securing distributed machine learning in high dimensions"
    - "generalized byzantine tolerant sgd"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "the internet of things secure distributed inference"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"

Connected through hyperedge HE-7 (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"
    - "being robust in high dimensions can be practical"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "byzantine tolerant machine learning"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"

Connected through hyperedge HE-8 (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"
    - "being robust in high dimensions can be practical"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "byzantine tolerant machine learning"
    - "resilience a criterion for learning in the presence of arbitrary outliers"

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)