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: "detox a redundancy based framework for faster and more robust gradient aggregation"
  Abstract: To improve the resilience of distributed training to worst-case, or Byzantine node failures, several recent approaches have replaced gradient averaging with robust aggregation methods. Such techniques can have high computational costs, often quadratic in the number of compute nodes, and only have limited robustness guarantees. Other methods have instead used redundancy to guarantee robustness, but can only tolerate limited number of Byzantine failures. In this work, we present DETOX, a Byzantine-resilient distributed training framework that combines algorithmic redundancy with robust aggregation. DETOX operates in two steps, a filtering step that uses limited redundancy to significantly reduce the effect of Byzantine nodes, and a hierarchical aggregation step that can be used in tandem with any state-of-the-art robust aggregation method. We show theoretically that this leads to a substantial increase in robustness, and has a per iteration runtime that can be nearly linear in the number of compute nodes. We provide extensive experiments over real distributed setups across a variety of large-scale machine learning tasks, showing that DETOX leads to orders of magnitude accuracy and speedup improvements over many state-of-the-art Byzantine-resilient approaches.

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"
    - "federated evaluation of on device personalization"
    - "measuring the effects of non identical data distribution for federated visual classification"
    - "adversarial initialization when your network performs the way i want"

Connected through hyperedge HE-2 (induced by source paper "distributed momentum for byzantine resilient learning"):
  with members:
    - "can you really backdoor federated learning"
    - "sgd decentralized byzantine resilience"
    - "momentum based variance reduction in non convex sgd"
    - "a little is enough circumventing defenses for distributed learning"
    - "byzantine stochastic gradient descent"
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "generalized byzantine tolerant sgd"
    - "byzantine robust distributed learning towards optimal statistical rates"

Connected through hyperedge HE-3 (induced by source paper "election coding for distributed learning protecting signsgd against byzantine attacks"):
  with members:
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "fast and secure distributed learning in high dimension"
    - "byzantine stochastic gradient descent"
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "speeding up distributed machine learning using codes"
    - "federated learning strategies for improving communication efficiency"
    - "hierarchical coding for distributed computing"

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"
    - "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"
    - "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 "fast machine learning with byzantine workers and servers"):
  with members:
    - "fall of empires breaking byzantine tolerant sgd by inner product manipulation"
    - "communication efficient distributed sgd with sketching"
    - "sgd decentralized byzantine resilience"
    - "a little is enough circumventing defenses for distributed learning"
    - "zeno byzantine suspicious stochastic gradient descent"
    - "generalized byzantine tolerant sgd"
    - "phocas dimensional byzantine resilient stochastic gradient descent"
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"

Connected through hyperedge HE-6 (induced by source paper "randomized reactive redundancy for byzantine fault tolerance in parallelized learning"):
  with members:
    - "doublesqueeze parallel stochastic gradient descent with double pass error compensated compression"
    - "byzantine fault tolerant distributed linear regression"
    - "fast machine learning with byzantine workers and servers"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "sever a robust meta algorithm for stochastic optimization"
    - "signsgd compressed optimisation for non convex problems"
    - "sparse communication for distributed gradient descent"
    - "distributed statistical machine learning in adversarial settings byzantine gradient descent"

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)