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: "evolving sdn for low power iot networks"
  Abstract: Software Defined Networking (SDN) offers a flexible and scalable architecture that abstracts decision making away from individual devices and provides a programmable network platform. Low-power wireless Internet of Things (IoT) networks, where multi-tenant and multi-application architectures require scalable and configurable solutions, are ideally placed to capitalize on this research. However, implementing a centralized SDN architecture within the constraints of a low-power wireless network faces considerable challenges. Not only is controller traffic subject to jitter due to unreliable links and network contention, but the overhead generated by SDN can severely affect the performance of other traffic. This paper addresses the challenge of bringing high-overhead SDN architecture to IEEE 802.15.4 networks. We explore how the traditional view of SDN needs to evolve in order to overcome the constraints of low-power wireless networks, and discuss protocol and architectural optimizations necessary to reduce SDN control overhead - the main barrier to successful implementation. Additionally, we argue that interoperability with the existing protocol stack is necessary to provide a platform for controller discovery, and coexistence with legacy networks. We consequently introduce  $\mu \text{SDN}$ , a lightweight SDN framework for Contiki OS with both IPv6 and underlying routing protocol interoperability, as well as optimizing a number of elements within the SDN architecture to reduce control overhead to practical levels. We evaluate  $\mu \text{SDN}$  in terms of latency, energy, and packet delivery. Through this evaluation we show how the cost of SDN control overhead (both bootstrapping and management) can be reduced to a point where comparable performance and scalability is achieved against an IEEE 802.15.4-2012 RPL-based network. Additionally, we demonstrate  $\mu \text{SDN}$  through simulation: providing a use-case where the SDN configurability can be used to provide Quality of Service (QoS) for critical network flows experiencing interference, and we achieve considerable reductions in delay and jitter in comparison to a scenario without SDN.

Connected through hyperedge HE-1 (induced by source paper "a pub sub sdn integrated framework for iot traffic orchestration"):
  with members:
    - "a publish subscribe qos aware framework for massive iot traffic orchestration"
    - "g bam a generalized bandwidth allocation model for ip mpls ds te networks"
    - "foggy a platform for workload orchestration in a fog computing environment"
    - "a hybrid approach for data analytics for internet of things"
    - "city data fusion sensor data fusion in the internet of things"
    - "software defined networking a comprehensive survey"
    - "alloctc sharing a new bandwidth allocation model for ds te networks"

Connected through hyperedge HE-2 (induced by source paper "atomic sdn is synchronous flooding the solution to software defined networking in iot"):
  with members:
    - "isolating sdn control traffic with layer 2 slicing in 6tisch industrial iot networks"
    - "software defined networking a comprehensive survey"

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.NI(Networking and Internet Architecture)