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: "distributed training with heterogeneous data bridging median and mean based algorithms"
  Abstract: Recently, there is a growing interest in the study of median-based algorithms for distributed non-convex optimization. Two prominent such algorithms include signSGD with majority vote, an effective approach for communication reduction via 1-bit compression on the local gradients, and medianSGD, an algorithm recently proposed to ensure robustness against Byzantine workers. The convergence analyses for these algorithms critically rely on the assumption that all the distributed data are drawn iid from the same distribution. However, in applications such as Federated Learning, the data across different nodes or machines can be inherently heterogeneous, which violates such an iid assumption. This work analyzes signSGD and medianSGD in distributed settings with heterogeneous data. We show that these algorithms are non-convergent whenever there is some disparity between the expected median and mean over the local gradients. To overcome this gap, we provide a novel gradient correction mechanism that perturbs the local gradients with noise, together with a series results that provable close the gap between mean and median of the gradients. The proposed methods largely preserve nice properties of these methods, such as the low per-iteration communication complexity of signSGD, and further enjoy global convergence to stationary solutions. Our perturbation technique can be of independent interest when one wishes to estimate mean through a median estimator.

Connected through hyperedge HE-1 (induced by source paper "salvaging federated learning by local adaptation"):
  with members:
    - "differential privacy has disparate impact on model accuracy"
    - "advances and open problems in federated learning"
    - "one shot federated learning"
    - "improving federated learning personalization via model agnostic meta learning"
    - "towards federated learning at scale system design"
    - "detox a redundancy based framework for faster and more robust gradient aggregation"
    - "federated evaluation of on device personalization"
    - "measuring the effects of non identical data distribution for federated visual classification"

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"
    - "byzantine stochastic gradient descent"
    - "rsa byzantine robust stochastic aggregation methods for distributed learning from heterogeneous datasets"
    - "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 "decentralized policy based private analytics"):
  with members:
    - "advances and open problems in federated learning"
    - "towards federated learning at scale system design"
    - "conclave secure multi party computation on big data"
    - "analyzing federated learning through an adversarial lens"
    - "federated learning strategies for improving communication efficiency"
    - "federated learning for mobile keyboard prediction"
    - "how to backdoor federated learning"
    - "mitigating location privacy attacks on mobile devices using dynamic app sandboxing"

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"
    - "analyzing federated learning through an adversarial lens"
    - "rsa byzantine robust stochastic aggregation methods for distributed learning from heterogeneous datasets"
    - "federated learning strategies for improving communication efficiency"
    - "federated optimization for heterogeneous networks"

Connected through hyperedge HE-5 (induced by source paper "how to backdoor federated learning"):
  with members:
    - "strip a defence against trojan attacks on deep neural networks"
    - "towards federated learning at scale system design"
    - "contamination attacks and mitigation in multi party machine learning"
    - "a little is enough circumventing defenses for distributed learning"
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "analyzing federated learning through an adversarial lens"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "multi party poisoning through generalized p tampering"

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)