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: "towards adaptive state consistency in distributed sdn control plane"
  Abstract: State synchronisation in clustered Software Defined Networking controller deployments ensures that all instances of the controller have the same state information in order to provide redundancy. Current implementations of controllers use a strong consistency model, where configuration changes must be synchronised across a number of instances before they are applied on the network infrastructure. For large deployments, this blocking process increases the delay of state synchronisation across cluster members and consequently has a detrimental effect on network operations that require rapid response, such as fast failover and Quality of Service applications. In this paper, we introduce an adaptive consistency model for SDN Controllers that employs concepts of eventual consistency models along with a novel ‘cost-based’ approach where strict synchronisation is employed for critical operations that affect a large portion of the network resources while less critical changes are periodically propagated across cluster nodes. We use simulation to evaluate our model and demonstrate the potential gains in performance.

Connected through hyperedge HE-1 (induced by source paper "survey of consistent software defined network updates"):
  with members:
    - "short schedules for fast flow rerouting"
    - "network slicing for 5g with sdn nfv concepts architectures and challenges"
    - "renaissance self stabilizing distributed sdn control plane"
    - "transiently consistent sdn updates being greedy is hard"
    - "the case for data plane timestamping in sdn"
    - "event driven network programming"
    - "congestion free rerouting of flows on dags"
    - "oneclock to rule them all using time in networked applications"

Connected through hyperedge HE-2 (induced by source paper "macs deep reinforcement learning based sdn controller synchronization policy design"):
  with members:
    - "survey of consistent software defined network updates"
    - "learning the optimal synchronization rates in distributed sdn control architectures"
    - "impact of adaptive consistency on distributed sdn applications an empirical study"
    - "dq scheduler deep reinforcement learning based controller synchronization in distributed sdn"
    - "action branching architectures for deep reinforcement learning"
    - "continuous control with deep reinforcement learning"
    - "dueling network architectures for deep reinforcement learning"
    - "software defined networking a comprehensive survey"

Connected through hyperedge HE-3 (induced by source paper "learning the optimal synchronization rates in distributed sdn control architectures"):
  with members:
    - "noisy submodular maximization via adaptive sampling with applications to crowdsourced image collection summarization"
    - "software defined networking a comprehensive survey"

Connected through hyperedge HE-4 (induced by source paper "impact of adaptive consistency on distributed sdn applications an empirical study"):
  with members:
    - "response time and availability study of raft consensus in distributed sdn control plane"
    - "dynamic switch controller association and control devolution for sdn systems"
    - "a clustering based consistency adaptation strategy for distributed sdn controllers"
    - "software defined networking a comprehensive survey"

Connected through hyperedge HE-5 (induced by source paper "morph an adaptive framework for efficient and byzantine fault tolerant sdn control plane"):
  with members:
    - "response time and availability study of raft consensus in distributed sdn control plane"
    - "dynamic switch controller association and control devolution for sdn systems"
    - "trusdn bootstrapping trust in cloud network infrastructure"

Connected through hyperedge HE-6 (induced by source paper "dq scheduler deep reinforcement learning based controller synchronization in distributed sdn"):
  with members:
    - "impact of adaptive consistency on distributed sdn applications an empirical study"
    - "ucb and infogain exploration via boldsymbol q ensembles"
    - "on the sample complexity of reinforcement learning with a generative model"

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)