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: "adaptive broadcast suppression for trickle based protocols"
  Abstract: Low-power wireless networks play an important role in the Internet of Things. Typically, these networks consist of a very large number of lossy and low-capacity devices, challenging the current state of the art in protocol design. In this context the Trickle algorithm plays an important role, serving as the basic mechanism for message dissemination in notable protocols such as RPL and MPL. While Trickle's broadcast suppression mechanism has been proven to be efficient, recent work has shown that it is intrinsically unfair in terms of load distribution and that its performance relies strongly on network topology. This can lead to increased end-to-end delays (MPL), or creation of sub-optimal routes (RPL). Furthermore, as highlighted in this work, there is no clear consensus within the research community about what the proper parameter settings of the suppression mechanism should be. We propose an extension to the Trickle algorithm, called adaptive-k, which allows nodes to individually adapt their suppression mechanism to local node density. Supported by analysis and a case study with RPL, we show that this extension allows for an easier configuration of Trickle, making it more robust to network topology.

Connected through hyperedge HE-1 (induced by source paper "a survey of limitations and enhancements of the ipv6 routing protocol for low power and lossy networks a focus on core operations"):
  with members:
    - "state of the art in power line communications from the applications to the medium"
    - "a comparative performance study of the routing protocols rpl loadng and loadng ctp with bidirectional traffic for ami scenario"
    - "multiple redundancy constants with trickle"
    - "wireless sensors networks for internet of things"

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)