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: "rsa byzantine robust stochastic aggregation methods for distributed learning from heterogeneous datasets"
  Abstract: In this paper, we propose a class of robust stochastic subgradient methods for distributed learning from heterogeneous datasets at presence of an unknown number of Byzantine workers. The Byzantine workers, during the learning process, may send arbitrary incorrect messages to the master due to data corruptions, communication failures or malicious attacks, and consequently bias the learned model. The key to the proposed methods is a regularization term incorporated with the objective function so as to robustify the learning task and mitigate the negative effects of Byzantine attacks. The resultant subgradient-based algorithms are termed Byzantine-Robust Stochastic Aggregation methods, justifying our acronym RSA used henceforth. In contrast to most of the existing algorithms, RSA does not rely on the assumption that the data are independent and identically distributed (i.i.d.) on the workers, and hence fits for a wider class of applications. Theoretically, we show that: i) RSA converges to a near-optimal solution with the learning error dependent on the number of Byzantine workers; ii) the convergence rate of RSA under Byzantine attacks is the same as that of the stochastic gradient descent method, which is free of Byzantine attacks. Numerically, experiments on real dataset corroborate the competitive performance of RSA and a complexity reduction compared to the state-of-the-art alternatives.

Connected through hyperedge HE-1 (induced by source paper "learning to detect malicious clients for robust federated learning"):
  with members:
    - "a survey on federated learning systems vision hype and reality for data privacy and protection"
    - "incentive design for efficient federated learning in mobile networks a contract theory approach"
    - "can you really backdoor federated learning"
    - "semi cyclic stochastic gradient descent"
    - "local model poisoning attacks to byzantine robust federated learning"
    - "federated learning challenges methods and future directions"
    - "federated variance reduced stochastic gradient descent with robustness to byzantine attacks"
    - "federated machine learning concept and applications"

Connected through hyperedge HE-2 (induced by source paper "stochastic sign sgd for federated learning with theoretical guarantees"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "slsgd secure and efficient distributed on device machine learning"
    - "communication efficient distributed blockwise momentum sgd with error feedback"
    - "distributed training with heterogeneous data bridging median and mean based algorithms"
    - "byzantine stochastic gradient descent"
    - "communication compression for decentralized training"
    - "generalized byzantine tolerant sgd"
    - "byzantine robust distributed learning towards optimal statistical rates"

Connected through hyperedge HE-3 (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"
    - "generalized byzantine tolerant sgd"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "federated learning with non iid data"
    - "defending against saddle point attack in byzantine robust distributed learning"
    - "learning mixtures of sparse linear regressions using sparse graph codes"
    - "being robust in high dimensions can be practical"

Connected through hyperedge HE-4 (induced by source paper "abnormal client behavior detection in federated learning"):
  with members:
    - "privacy preserving deep learning via weight transmission"
    - "towards federated learning at scale system design"
    - "incentive design for efficient federated learning in mobile networks a contract theory approach"
    - "federated machine learning concept and applications"
    - "distributed training with heterogeneous data bridging median and mean based algorithms"
    - "analyzing federated learning through an adversarial lens"
    - "federated learning strategies for improving communication efficiency"
    - "federated optimization for heterogeneous networks"

Connected through hyperedge HE-5 (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"
    - "securing distributed machine learning in high dimensions"
    - "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-6 (induced by source paper "secure distributed on device learning networks with byzantine adversaries"):
  with members:
    - "supervised learning under distributed features"
    - "byzantine stochastic gradient descent"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"
    - "proximity without consensus in online multiagent optimization"
    - "federated multi task learning"
    - "distributed optimization under adversarial nodes"
    - "federated optimization distributed optimization beyond the datacenter"

Connected through hyperedge HE-7 (induced by source paper "spocc scalable possibilistic classifier combination toward robust aggregation of classifiers"):
  with members:
    - "online hierarchical clustering approximations"
    - "federated learning with unbiased gradient aggregation and controllable meta updating"
    - "on single source robustness in deep fusion models"
    - "possibilistic logic bases and possibilistic graphs"
    - "automated reasoning using possibilistic logic semantics belief revision and variable certainty weights"

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)