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: "average consensus on general strongly connected digraphs"
  Abstract: We study the average consensus problem of multi-agent systems for general network topologies with unidirectional information flow. We propose two (linear) distributed algorithms, deterministic and gossip, respectively for the cases where the inter-agent communication is synchronous and asynchronous. Our contribution is that in both cases, the developed algorithms guarantee state averaging on arbitrary strongly connected digraphs; in particular, this graphical condition does not require that the network be balanced or symmetric, thereby extending many previous results in the literature. The key novelty of our approach is to augment an additional variable for each agent, called "surplus", whose function is to locally record individual state updates. For convergence analysis, we employ graph-theoretic and nonnegative matrix tools, with the eigenvalue perturbation theory playing a crucial role.

Connected through hyperedge HE-1 (induced by source paper "distributed nesterov gradient methods over arbitrary graphs"):
  with members:
    - "multi agent reinforcement learning via double averaging primal dual optimization"
    - "convergence rates of distributed nesterov like gradient methods on random networks"
    - "fast distributed gradient methods"

Connected through hyperedge HE-2 (induced by source paper "asyspa an exact asynchronous algorithm for convex optimization over digraphs"):
  with members:
    - "distributed discrete time optimization in multiagent networks using only sign of relative state"
    - "asynchronous decentralized optimization in directed networks"
    - "asynchronous subgradient push"
    - "distributed convex optimization with inequality constraints over time varying unbalanced digraphs"
    - "distributed nonconvex multiagent optimization over time varying networks"
    - "asynchronous distributed optimization via randomized dual proximal gradient"
    - "asynchronous adaptation and learning over networks part i modeling and stability analysis"

Connected through hyperedge HE-3 (induced by source paper "a distributed algorithm for least square solutions of linear equations"):
  with members:
    - "decentralized gradient algorithm for solution of a linear equation"
    - "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"
    - "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 "distributed random fixed projected algorithm for constrained optimization over digraphs"):
  with members:
    - "networked parallel algorithms for robust convex optimization via the scenario approach"
    - "average consensus on arbitrary strongly connected digraphs with time varying topologies"

Connected through hyperedge HE-5 (induced by source paper "distributed convex optimization with inequality constraints over time varying unbalanced digraphs"):
  with members:
    - "networked parallel algorithms for robust convex optimization via the scenario approach"
    - "distributed random fixed projected algorithm for constrained optimization over digraphs"
    - "distributed nonconvex multiagent optimization over time varying networks"
    - "explicit convergence rate of a distributed alternating direction method of multipliers"

Connected through hyperedge HE-6 (induced by source paper "push sum with transmission failures"):
  with members:
    - "analysis of sum weight like algorithms for averaging in wireless sensor networks"
    - "gossip algorithms for distributed signal processing"
    - "distributed consensus algorithms in sensor networks quantized data and random link failures"

Connected through hyperedge HE-7 (induced by source paper "distributed parameter estimation with quantized communication via running average"):
  with members:
    - "distributed consensus algorithms in sensor networks quantized data and random link failures"

Connected through hyperedge HE-8 (induced by source paper "broadcast gossip algorithms for consensus on strongly connected digraphs"):
  with members:
    - "gossip algorithms for distributed signal processing"

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)