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 "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.

Includes members:
  Member "introduction to online convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "bandit convex optimization for scalable and dynamic iot management":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bandit convex optimization in non stationary environments")
  Member "optimal and scalable caching for 5g using reinforcement learning of space time popularities":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "online convex optimization for caching networks")
  Member "multi timescale online optimization of network function virtualization for service chaining":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a dynamic reliability aware service placement for network function virtualization nfv")
  Member "fully decentralized multi agent reinforcement learning with networked agents":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cluster based social reinforcement learning")
  Member "joint cache resource allocation and request routing for in network caching services":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "edgemore improving resource allocation with multiple options from tenants")
  Member "joint trajectory and communication design for multi uav enabled wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "joint trajectory and resource allocation design for energy efficient secure uav communication systems")
  Member "stochastic joint radio and computational resource management for multi user mobile edge computing systems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi agent meta reinforcement learning for self powered and sustainable edge computing systems")

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)