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: "long range communications in unlicensed bands the rising stars in the iot and smart city scenarios"
  Abstract: Connectivity is probably the most basic building block of the Internet of Things (IoT) paradigm. Up to know, the two main approaches to provide data access to the \emph{things} have been based either on multi-hop mesh networks using short-range communication technologies in the unlicensed spectrum, or on long-range, legacy cellular technologies, mainly 2G/GSM, operating in the corresponding licensed frequency bands. Recently, these reference models have been challenged by a new type of wireless connectivity, characterized by low-rate, long-range transmission technologies in the unlicensed sub-GHz frequency bands, used to realize access networks with star topology which are referred to a \emph{Low-Power Wide Area Networks} (LPWANs). In this paper, we introduce this new approach to provide connectivity in the IoT scenario, discussing its advantages over the established paradigms in terms of efficiency, effectiveness, and architectural design, in particular for the typical Smart Cities applications.

Connected through hyperedge HE-1 (induced by source paper "performance analysis of single cell adaptive data rate enabled lorawan"):
  with members:
    - "how agile is the adaptive data rate mechanism of lorawan"
    - "scalability analysis of a lora network under imperfect orthogonality"
    - "fair adaptive data rate allocation and power control in lorawan"
    - "low power wide area network analysis can lora scale"
    - "on stochastic geometry modeling of cellular uplink transmission with truncated channel inversion power control"

Connected through hyperedge HE-2 (induced by source paper "spectrum sharing for massive access in ultra narrowband iot systems"):
  with members:
    - "sparse signal processing for grant free massive connectivity a future paradigm for random access protocols in the internet of things"
    - "modeling and analysis of cellular networks using stochastic geometry a tutorial"
    - "aggregation and resource scheduling in machine type communication networks a stochastic geometry approach"
    - "low power wide area networks an overview"
    - "transmission capacity of wireless networks"

Connected through hyperedge HE-3 (induced by source paper "lora beyond aloha an investigation of alternative random access protocols"):
  with members:
    - "analysis of lorawan uplink with multiple demodulating paths and capture effect"
    - "slotted aloha on lorawan design analysis and deployment"
    - "scalability analysis of a lora network under imperfect orthogonality"
    - "low power wide area network analysis can lora scale"

Connected through hyperedge HE-4 (induced by source paper "hybrid coded replication in lora networks"):
  with members:
    - "scalability analysis of a lora network under imperfect orthogonality"
    - "application layer coding for iot benefits limitations and implementation aspects"
    - "low power wide area network analysis can lora scale"
    - "low power wide area networks an overview"

Connected through hyperedge HE-5 (induced by source paper "lora in the field insights from networking the smart city hamburg with riot"):
  with members:
    - "lorawan in the wild measurements from the things network"

Connected through hyperedge HE-6 (induced by source paper "non bayesian activity detection large scale fading coefficient estimation and unsourced random access with a massive mimo receiver"):
  with members:
    - "improved scaling law for activity detection in massive mimo systems"
    - "on the fly large scale channel gain estimation for massive antenna array base stations"
    - "techniques for improving the finite length performance of sparse superposition codes"
    - "on the restricted isometry of the columnwise khatri rao product"
    - "sparse activity detection for massive connectivity"
    - "massive connectivity with massive mimo part ii achievable rate characterization"
    - "sparse signal processing for grant free massive connectivity a future paradigm for random access protocols in the internet of things"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"

Connected through hyperedge HE-7 (induced by source paper "sparcs for unsourced random access"):
  with members:
    - "massive mimo unsourced random access"
    - "techniques for improving the finite length performance of sparse superposition codes"
    - "sparse signal processing for grant free massive iot connectivity"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "on or many access channels"
    - "device activity and embedded information bit detection using amp in massive mimo"
    - "state evolution for approximate message passing with non separable functions"

Connected through hyperedge HE-8 (induced by source paper "grant free massive random access with a massive mimo receiver"):
  with members:
    - "phase transition analysis for covariance based massive random access with massive mimo"
    - "non bayesian activity detection large scale fading coefficient estimation and unsourced random access with a massive mimo receiver"
    - "on the restricted isometry property of centered self khatri rao products"
    - "polar coding and random spreading for unsourced multiple access"
    - "an enhanced decoding algorithm for coded compressed sensing"
    - "improved scaling law for activity detection in massive mimo systems"
    - "on the fly large scale channel gain estimation for massive antenna array base stations"
    - "on the restricted isometry of the columnwise khatri rao product"

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)