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: "local model poisoning attacks to byzantine robust federated learning"
  Abstract: In federated learning, multiple client devices jointly learn a machine learning model: each client device maintains a local model for its local training dataset, while a master device maintains a global model via aggregating the local models from the client devices. The machine learning community recently proposed several federated learning methods that were claimed to be robust against Byzantine failures (e.g., system failures, adversarial manipulations) of certain client devices. In this work, we perform the first systematic study on local model poisoning attacks to federated learning. We assume an attacker has compromised some client devices, and the attacker manipulates the local model parameters on the compromised client devices during the learning process such that the global model has a large testing error rate. We formulate our attacks as optimization problems and apply our attacks to four recent Byzantine-robust federated learning methods. Our empirical results on four real-world datasets show that our attacks can substantially increase the error rates of the models learnt by the federated learning methods that were claimed to be robust against Byzantine failures of some client devices. We generalize two defenses for data poisoning attacks to defend against our local model poisoning attacks. Our evaluation results show that one defense can effectively defend against our attacks in some cases, but the defenses are not effective enough in other cases, highlighting the need for new defenses against our local model poisoning attacks to federated learning.

Connected through hyperedge HE-1 (induced by source paper "turbo aggregate breaking the quadratic aggregation barrier in secure federated learning"):
  with members:
    - "advances and open problems in federated learning"
    - "central server free federated learning over single sided trust social networks"
    - "towards federated learning at scale system design"
    - "can you really backdoor federated learning"
    - "semi cyclic stochastic gradient descent"
    - "peer to peer federated learning on graphs"
    - "federated learning challenges methods and future directions"
    - "agnostic federated learning"

Connected through hyperedge HE-2 (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"
    - "federated learning challenges methods and future directions"
    - "federated variance reduced stochastic gradient descent with robustness to byzantine attacks"
    - "federated machine learning concept and applications"
    - "fedmd heterogenous federated learning via model distillation"

Connected through hyperedge HE-3 (induced by source paper "influence function based data poisoning attacks to top n recommender systems"):
  with members:
    - "private and communication efficient edge learning a sparse differential gaussian masking distributed sgd approach"
    - "byzantine resilient stochastic gradient descent for distributed learning a lipschitz inspired coordinate wise median approach"
    - "on the accuracy of influence functions for measuring group effects"
    - "attacking graph based classification via manipulating the graph structure"
    - "poisoning attacks to graph based recommender systems"
    - "data dropout optimizing training data for convolutional neural networks"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "manipulating machine learning poisoning attacks and countermeasures for regression learning"

Connected through hyperedge HE-4 (induced by source paper "towards byzantine resilient learning in decentralized systems"):
  with members:
    - "zeno robust fully asynchronous sgd"
    - "slsgd secure and efficient distributed on device machine learning"
    - "towards federated learning at scale system design"
    - "e2 train training state of the art cnns with over 80 energy savings"
    - "zeno robust asynchronous sgd with arbitrary number of byzantine workers"
    - "a little is enough circumventing defenses for distributed learning"
    - "communication compression for decentralized training"
    - "generalized byzantine tolerant sgd"

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.CR(Cryptography and Security)