human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "robust decentralized state estimation and tracking for power systems via network gossiping"):
  Abstract: This paper proposes a fully decentralized adaptive re-weighted state estimation (DARSE) scheme for power systems via network gossiping. The enabling technique is the proposed Gossip-based Gauss-Newton (GGN) algorithm, which allows to harness the computation capability of each area (i.e. a database server that accrues data from local sensors) to collaboratively solve for an accurate global state. The DARSE scheme mitigates the influence of bad data by updating their error variances online and re-weighting their contributions adaptively for state estimation. Thus, the global state can be estimated and tracked robustly using near-neighbor communications in each area. Compared to other distributed state estimation techniques, our communication model is flexible with respect to reconfigurations and resilient to random failures as long as the communication network is connected. Furthermore, we prove that the Jacobian of the power flow equations satisfies the Lipschitz condition that is essential for the GGN algorithm to converge to the desired solution. Simulations of the IEEE-118 system show that the DARSE scheme can estimate and track online the global power system state accurately, and degrades gracefully when there are random failures and bad data.

Includes members:
  Member "sparse recovery from nonlinear measurements with applications in bad data detection for power networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "outlier robust extreme learning machine for multi target regression")
  Member "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "distributed learning in the non convex world from batch to streaming data and beyond")

Question: Please tell me which class the source paper 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.DC(Distributed, Parallel, and Cluster Computing)