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: "learning and management for internet of things accounting for adaptivity and scalability"
  Abstract: Internet-of-Things (IoT) envisions an intelligent infrastructure of networked smart devices offering task-specific monitoring and control services. The unique features of IoT include extreme heterogeneity, massive number of devices, and unpredictable dynamics partially due to human interaction. These call for foundational innovations in network design and management. Ideally, it should allow efficient adaptation to changing environments, and low-cost implementation scalable to massive number of devices, subject to stringent latency constraints. To this end, the overarching goal of this paper is to outline a unified framework for online learning and management policies in IoT through joint advances in communication, networking, learning, and optimization. From the network architecture vantage point, the unified framework leverages a promising fog architecture that enables smart devices to have proximity access to cloud functionalities at the network edge, along the cloud-to-things continuum. From the algorithmic perspective, key innovations target online approaches adaptive to different degrees of nonstationarity in IoT dynamics, and their scalable model-free implementation under limited feedback that motivates blind or bandit approaches. The proposed framework aspires to offer a stepping stone that leads to systematic designs and analysis of task-specific learning and management schemes for IoT, along with a host of new research directions to build on.

Connected through hyperedge HE-1 (induced by source paper "optimal design of queuing systems via compositional stochastic programming"):
  with members:
    - "stochastic gradient descent with biased but consistent gradient estimators"
    - "sla oriented resource provisioning for cloud computing challenges architecture and solutions"

Connected through hyperedge HE-2 (induced by source paper "communication efficient distributed reinforcement learning"):
  with members:
    - "dynamic power distribution system management with a locally connected communication network"
    - "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:
    - "dynamic power distribution system management with a locally connected communication network"
    - "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"

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)