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: "device to device communication in cellular networks"
  Abstract: A constant need to increase the network capacity for meeting the growing demands of the subscribers has led to the evolution of cellular communication networks from the first generation (1G) to the fifth generation (5G). There will be billions of connected devices in the near future. Such a large number of connections are expected to be heterogeneous in nature, demanding higher data rates, lesser delays, enhanced system capacity and superior throughput. The available spectrum resources are limited and need to be flexibly used by the mobile network operators (MNOs) to cope with the rising demands. An emerging facilitator of the upcoming high data rate demanding next generation networks (NGNs) is device-to-device (D2D) communication. An extensive survey on device-to-device (D2D) communication has been presented in this paper, including the plus points it offers; the key open issues associated with it like peer discovery, resource allocation etc, demanding special attention of the research community; some of its integrant technologies like millimeter wave D2D (mmWave), ultra dense networks (UDNs), cognitive D2D, handover procedure in D2D and its numerous use cases. Architecture is suggested aiming to fulfill all the subscriber demands in an optimal manner. The Appendix mentions some ongoing standardization activities and research projects of D2D communication. Proposed architecture for resource allocation in device-to-device (D2D) communication. In order to meet the rising subscriber demands and provide them satisfactory services, D2D communication is being looked upon as an emerging technology of the next generation networks. For optimal sharing of resources between the D2D users and cellular users in the cellular networks, a sectored antenna is used at the base station, dividing the entire coverage area into three sectors of 120° each. This supports reduction in interference between the two types of users thereby, improving system performance.Display Omitted Roadmap of D2D communication through the generations of wireless communication.Outline of D2D communication; supporting architectures and use cases.Integrant features (like Ultra Dense networks, millimeter wave communication, cooperative communication, network coding etc.) to enhance utility of D2D communication in the next generation networks.Key open challenges and some important algorithms to overcome them.Proposed architecture for optimal resource allocation and throughput maximization.

Connected through hyperedge HE-1 (induced by source paper "distributed artificial intelligence solution for d2d communication in 5g networks"):
  with members:
    - "a survey of conventional and artificial intelligence learning based resource allocation and interference mitigation schemes in d2d enabled networks"
    - "5g cellular key enabling technologies and research challenges"
    - "energy efficient resource allocation for device to device underlay communication"

Connected through hyperedge HE-2 (induced by source paper "on the insecurities of mobile d2d file sharing applications"):
  with members:
    - "the android platform security model"
    - "survey and systematization of secure device pairing"

Connected through hyperedge HE-3 (induced by source paper "sector based radio resource allocation sbrra algorithm for better quality of service and experience in device to device d2d communication"):
  with members:
    - "optimal resource allocation in multicast device to device communications underlaying lte networks"
    - "limited feedback scheme for device to device communications in 5g cellular networks with reliability and cellular secrecy outage constraints"
    - "hybrid centralized distributed resource allocation for device to device communication underlaying cellular networks"
    - "a survey of energy efficient techniques for 5g networks and challenges ahead"
    - "what will 5g be"
    - "five disruptive technology directions for 5g"

Connected through hyperedge HE-4 (induced by source paper "the influence of canyon shadowing on device to device connectivity in urban scenario"):
  with members:
    - "percolation for d2d networks on street systems"
    - "a survey on device to device communication in cellular networks"
    - "spectrum sharing for device to device communication in cellular networks"
    - "what will 5g be"
    - "five disruptive technology directions for 5g"

Connected through hyperedge HE-5 (induced by source paper "security for 4g and 5g cellular networks a survey of existing authentication and privacy preserving schemes"):
  with members:
    - "cloudifying the 3gpp ip multimedia subsystem for 4g and beyond a survey"
    - "privacy preserving schemes for ad hoc social networks a survey"
    - "user association in 5g networks a survey and an outlook"
    - "a survey of downlink non orthogonal multiple access for 5g wireless communication networks"
    - "survey of promising technologies for 5g networks"
    - "assessment of lte wireless access for monitoring of energy distribution in the smart grid"
    - "securefind secure and privacy preserving object finding via mobile crowdsourcing"
    - "the random oracle methodology revisited"

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)