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 "network flows that solve linear equations"):
  Abstract: We study distributed network flows as solvers in continuous time for the linear algebraic equation $\mathbf{z}=\mathbf{H}\mathbf{y}$. Each node $i$ has access to a row $\mathbf{h}_i^{\rm T}$ of the matrix $\mathbf{H}$ and the corresponding entry $z_i$ in the vector $\mathbf{z}$. The first "consensus + projection" flow under investigation consists of two terms, one from standard consensus dynamics and the other contributing to projection onto each affine subspace specified by the $\mathbf{h}_i$ and $z_i$. The second "projection consensus" flow on the other hand simply replaces the relative state feedback in consensus dynamics with projected relative state feedback. Without dwell-time assumption on switching graphs as well as without positively lower bounded assumption on arc weights, we prove that all node states converge to a common solution of the linear algebraic equation, if there is any. The convergence is global for the "consensus + projection" flow while local for the "projection consensus" flow in the sense that the initial values must lie on the affine subspaces. If the linear equation has no exact solutions, we show that the node states can converge to a ball around the least squares solution whose radius can be made arbitrarily small through selecting a sufficiently large gain for the "consensus + projection" flow under fixed bidirectional graphs. Semi-global convergence to approximate least squares solutions is demonstrated for general switching directed graphs under suitable conditions. It is also shown that the "projection consensus" flow drives the average of the node states to the least squares solution with complete graph. Numerical examples are provided as illustrations of the established results.

Includes members:
  Member "decentralized gradient algorithm for solution of a linear equation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dynamical privacy in distributed computing part i privacy loss and ppsc mechanism")
  Member "a distributed algorithm for solving a linear algebraic equation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "lyapunov criterion for stochastic systems and its applications in distributed computation")
  Member "a fast distributed solver for symmetric diagonally dominant linear equations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a distributed laplacian solver and its applications to electrical flow and random spanning tree computation")
  Member "solving systems of linear equations locally and asynchronously":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the role of clustering in personalized pagerank estimation")
  Member "distributed strongly convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "clustering with distributed data")
  Member "fast distributed gradient methods":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on local computation for optimization in multi agent systems")
  Member "cooperative convex optimization in networked systems augmented lagrangian algorithms with directed gossip communication":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "decentralized multi agent reinforcement learning with networked agents recent advances")
  Member "randomized optimal consensus of multi agent systems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "distributed continuous time approximate projection protocols for shortest distance optimization problems")

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.SY(Systems and Control)