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: "mitigating sybils in federated learning poisoning"
  Abstract: Machine learning (ML) over distributed data is relevant to a variety of domains. Existing approaches, such as federated learning, compose the outputs computed by a group of devices at a central aggregator and run multi-round algorithms to generate a globally shared model. Unfortunately, such approaches are susceptible to a variety of attacks, including model poisoning, which is made substantially worse in the presence of sybils. #R##N#In this paper we first evaluate the vulnerability of federated learning to sybil-based poisoning attacks. We then describe FoolsGold, a novel defense to this problem that identifies poisoning sybils based on the diversity of client contributions in the distributed learning process. Unlike prior work, our system does not assume that the attackers are in the minority, requires no auxiliary information outside of the learning process, and makes fewer assumptions about clients and their data. #R##N#In our evaluation we show that FoolsGold exceeds the capabilities of existing state of the art approaches to countering ML poisoning attacks. Our results hold for a variety of conditions, including different distributions of data, varying poisoning targets, and various attack strategies.

Connected through hyperedge HE-1 (induced by source paper "threats to federated learning a survey"):
  with members:
    - "fedvision an online visual object detection platform powered by federated learning"
    - "idlg improved deep leakage from gradients"
    - "advances and open problems in federated learning"
    - "deep leakage from gradients"
    - "towards fair and decentralized privacy preserving deep learning with blockchain"
    - "federated learning challenges methods and future directions"
    - "federated machine learning concept and applications"
    - "analyzing federated learning through an adversarial lens"

Connected through hyperedge HE-2 (induced by source paper "data selection for federated learning with relevant and irrelevant data at clients"):
  with members:
    - "advances and open problems in federated learning"
    - "how does disagreement help generalization against label corruption"
    - "federated learning challenges methods and future directions"
    - "federated machine learning concept and applications"
    - "wireless network intelligence at the edge"
    - "beyond inferring class representatives user level privacy leakage from federated learning"
    - "leaf a benchmark for federated settings"
    - "co teaching robust training of deep neural networks with extremely noisy labels"

Connected through hyperedge HE-3 (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"
    - "defending against saddle point attack in byzantine robust distributed learning"
    - "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-4 (induced by source paper "local model poisoning attacks to byzantine robust federated learning"):
  with members:
    - "attacking graph based classification via manipulating the graph structure"
    - "analyzing federated learning through an adversarial lens"
    - "exploiting unintended feature leakage in collaborative learning"
    - "federated learning strategies for improving communication efficiency"
    - "poisoning attacks to graph based recommender systems"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"
    - "is feature selection secure against training data poisoning"

Connected through hyperedge HE-5 (induced by source paper "a federated learning approach for mobile packet classification"):
  with members:
    - "one shot federated learning"
    - "towards federated learning at scale system design"
    - "client selection for federated learning with heterogeneous resources in mobile edge"
    - "federated heavy hitters discovery with differential privacy"
    - "analyzing federated learning through an adversarial lens"
    - "exploiting unintended feature leakage in collaborative learning"
    - "federated learning strategies for improving communication efficiency"
    - "beyond inferring class representatives user level privacy leakage from federated learning"

Connected through hyperedge HE-6 (induced by source paper "eavesdrop the composition proportion of training labels in federated learning"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "a little is enough circumventing defenses for distributed learning"
    - "updates leak data set inference and reconstruction attacks in online learning"
    - "federated learning for emoji prediction in a mobile keyboard"
    - "analyzing federated learning through an adversarial lens"
    - "exploiting unintended feature leakage in collaborative learning"
    - "multi party poisoning through generalized p tampering"
    - "federated learning for ultra reliable low latency v2v communications"

Connected through hyperedge HE-7 (induced by source paper "robust learning from untrusted sources"):
  with members:
    - "byzantine stochastic gradient descent"
    - "algorithms and theory for multiple source adaptation"
    - "the convergence of stochastic gradient descent in asynchronous shared memory"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "improving the generalization of adversarial training with domain adaptation"
    - "using trusted data to train deep networks on labels corrupted by severe noise"
    - "on fundamental limits of robust learning"
    - "learning discrete distributions from untrusted batches"

Connected through hyperedge HE-8 (induced by source paper "shielding collaborative learning mitigating poisoning attacks through client side detection"):
  with members:
    - "analyzing federated learning through an adversarial lens"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "model reuse attacks on deep learning systems"
    - "federated learning strategies for improving communication efficiency"
    - "privacy preserving collaborative deep learning with unreliable participants"
    - "backdooring convolutional neural networks via targeted weight perturbations"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "how to backdoor federated learning"

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)