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: "finite time guarantees for byzantine resilient distributed state estimation with noisy measurements"
  Abstract: This work considers resilient, cooperative state estimation in unreliable multi-agent networks. A network of agents aims to collaboratively estimate the value of an unknown vector parameter, while an {\em unknown} subset of agents suffer Byzantine faults. Faulty agents malfunction arbitrarily and may send out {\em highly unstructured} messages to other agents in the network. As opposed to fault-free networks, reaching agreement in the presence of Byzantine faults is far from trivial. In this paper, we propose a computationally-efficient algorithm that is provably robust to Byzantine faults. At each iteration of the algorithm, a good agent (1) performs a gradient descent update based on noisy local measurements, (2) exchanges its update with other agents in its neighborhood, and (3) robustly aggregates the received messages using coordinate-wise trimmed means. Under mild technical assumptions, we establish that good agents learn the true parameter asymptotically in almost sure sense. We further complement our analysis by proving (high probability) {\em finite-time} convergence rate, encapsulating network characteristics.

Connected through hyperedge HE-1 (induced by source paper "sgd decentralized byzantine resilience"):
  with members:
    - "distributed learning with adversarial agents under relaxed network condition"
    - "byzantine stochastic gradient descent"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "zeno byzantine suspicious stochastic gradient descent"
    - "federated learning strategies for improving communication efficiency"
    - "adversarial spheres"
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"

Connected through hyperedge HE-2 (induced by source paper "byzantine fault tolerant distributed linear regression"):
  with members:
    - "secure state estimation with byzantine sensors a probabilistic approach"
    - "byzantine stochastic gradient descent"
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "byzantine resilient distributed observers for lti systems"
    - "generalized byzantine tolerant sgd"
    - "byzantine robust distributed learning towards optimal statistical rates"
    - "sever a robust meta algorithm for stochastic optimization"
    - "byrdie byzantine resilient distributed coordinate descent for decentralized 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.SY(Systems and Control)