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: "decentralized gradient algorithm for solution of a linear equation"
  Abstract: The paper develops a technique for solving a linear equation $Ax=b$ with a square and nonsingular matrix $A$, using a decentralized gradient algorithm. In the language of control theory, there are $n$ agents, each storing at time $t$ an $n$-vector, call it $x_i(t)$, and a graphical structure associating with each agent a vertex of a fixed, undirected and connected but otherwise arbitrary graph $\mathcal G$ with vertex set and edge set $\mathcal V$ and $\mathcal E$ respectively. We provide differential equation update laws for the $x_i$ with the property that each $x_i$ converges to the solution of the linear equation exponentially fast. The equation for $x_i$ includes additive terms weighting those $x_j$ for which vertices in $\mathcal G$ corresponding to the $i$-th and $j$-th agents are adjacent. The results are extended to the case where $A$ is not square but has full row rank, and bounds are given on the convergence rate.

Connected through hyperedge HE-1 (induced by source paper "dynamical privacy in distributed computing part i privacy loss and ppsc mechanism"):
  with members:
    - "a distributed algorithm for solving a linear algebraic equation"
    - "distributed sensor localization in random environments using minimal number of anchor nodes"

Connected through hyperedge HE-2 (induced by source paper "a distributed algorithm for solving linear algebraic equations over random networks"):
  with members:
    - "a distributed algorithm for solving a linear algebraic equation"
    - "a distributed algorithm for solving positive definite linear equations over networks with membership dynamics"
    - "distributed recursive least squares stability and performance analysis"

Connected through hyperedge HE-3 (induced by source paper "a distributed algorithm for least square solutions of linear equations"):
  with members:
    - "on the stability and convergence of a class of consensus systems with a nonlinear input"
    - "a distributed algorithm for solving a linear algebraic equation"
    - "distributed evaluation and convergence of self appraisals in social networks"
    - "distributed constrained optimization by consensus based primal dual perturbation method"
    - "a distributed algorithm for solving positive definite linear equations over networks with membership dynamics"
    - "average consensus on general strongly connected digraphs"
    - "fast distributed gradient methods"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"

Connected through hyperedge HE-4 (induced by source paper "an arrow hurwicz uzawa type flow as least squares solver for network linear equations"):
  with members:
    - "a distributed algorithm for solving a linear algebraic equation"
    - "network flows that solve linear equations"
    - "parallel implementations of the jacobi linear algebraic systems solve"
    - "fast distributed gradient methods"

Connected through hyperedge HE-5 (induced by source paper "finite time distributed linear equation solver for minimum l_1 norm solutions"):
  with members:
    - "a distributed algorithm for solving a linear algebraic equation"
    - "a distributed algorithm for solving positive definite linear equations over networks with membership dynamics"
    - "distributed compressive sensing"

Connected through hyperedge HE-6 (induced by source paper "a double layered framework for distributed coordination in solving linear equations"):
  with members:
    - "deep learning in neural networks"
    - "a distributed algorithm for solving a linear algebraic equation"
    - "distributed constrained optimization by consensus based primal dual perturbation method"

Connected through hyperedge HE-7 (induced by source paper "network flows that solve linear equations"):
  with members:
    - "a distributed algorithm for solving a linear algebraic equation"
    - "a fast distributed solver for symmetric diagonally dominant linear equations"
    - "solving systems of linear equations locally and asynchronously"
    - "distributed strongly convex optimization"
    - "fast distributed gradient methods"
    - "cooperative convex optimization in networked systems augmented lagrangian algorithms with directed gossip communication"
    - "randomized optimal consensus of multi agent systems"
    - "reaching an optimal consensus dynamical systems that compute intersections of convex sets"

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)