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: "dynamic power distribution system management with a locally connected communication network"
  Abstract: Coordinated optimization and control of distribution-level assets enables a reliable and optimal integration of massive amount of distributed energy resources (DERs) and facilitates distribution system management (DSM). Accordingly, the objective is to coordinate the power injection at the DERs to maintain certain quantities across the network, e.g., voltage magnitude, line flows, and line losses, to be close to a desired profile. By and large, the performance of the DSM algorithms has been challenged by two factors: 1) the possibly nonstrongly connected communication network over DERs that hinders the coordination; and 2) the dynamics of the real system caused by the DERs with heterogeneous capabilities, time-varying operating conditions, and real-time measurement mismatches. In this paper, we investigate the modeling and algorithm design and analysis with the consideration of these two factors. In particular, a game-theoretic characterization is first proposed to account for a locally connected communication network over DERs, along with the analysis of the existence and uniqueness of the Nash equilibrium therein. To achieve the equilibrium in a distributed fashion, a  projected-gradient -based asynchronous DSM algorithm is then advocated. The algorithm performance, including the convergence speed and the tracking error, is analytically guaranteed under the dynamic setting. Extensive numerical tests on both synthetic and realistic cases corroborate the analytical results derived.

Connected through hyperedge HE-1 (induced by source paper "finite sample analysis for decentralized batch multi agent reinforcement learning with networked agents"):
  with members:
    - "a multi agent off policy actor critic algorithm for distributed reinforcement learning"
    - "a theoretical analysis of deep q learning"
    - "policy optimization provably converges to nash equilibria in zero sum linear quadratic games"
    - "information theoretic considerations in batch reinforcement learning"
    - "a finite time analysis of temporal difference learning with linear function approximation"
    - "communication efficient distributed reinforcement learning"
    - "distributed equilibrium learning for power network voltage control with a locally connected communication network"
    - "distributed learning of average belief over networks using sequential observations"

Connected through hyperedge HE-2 (induced by source paper "communication efficient distributed reinforcement learning"):
  with members:
    - "learning and management for internet of things accounting for adaptivity and scalability"
    - "stochastic variance reduced policy gradient"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "proximal policy optimization algorithms"
    - "a berkeley view of systems challenges for ai"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "safe multi agent reinforcement learning for autonomous driving"

Connected through hyperedge HE-3 (induced by source paper "distributed learning of average belief over networks using sequential observations"):
  with members:
    - "learning and management for internet of things accounting for adaptivity and scalability"
    - "distributed equilibrium learning for power network voltage control with a locally connected communication network"
    - "fully decentralized multi agent reinforcement learning with networked agents"
    - "fundamental limits of online and distributed algorithms for statistical learning and estimation"
    - "distributed parameter estimation in sensor networks nonlinear observation models and imperfect communication"

Connected through hyperedge HE-4 (induced by source paper "control of learning in anti coordination network games"):
  with members:
    - "interdependent security games on networks under behavioral probability weighting"

Connected through hyperedge HE-5 (induced by source paper "power traffic coordinated operation for bi peak shaving and bi ramp smoothing a hierarchical data driven approach"):
  with members:
    - "learning the lmp load coupling from data a support vector machine based approach"

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)