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: "aggregation and trunking of m2m traffic via d2d connections"
  Abstract: Machine-to-Machine (M2M) communications is one of the key enablers of the Internet of Things (IoT). Billions of devices are expected to be deployed in the next future for novel M2M applications demanding ubiquitous access and global connectivity. In order to cope with the massive number of machines, there is a need for new techniques to coordinate the access and allocate the resources. Although the majority of the proposed solutions are focused on the adaptation of the traditional cellular networks to the M2M traffic patterns, novel approaches based on the direct communication among nearby devices may represent an effective way to avoid access congestion and cell overload. In this paper, we propose a new strategy inspired by the classical Trunked Radio Systems (TRS), exploiting the Device-to-Device (D2D) connectivity between cellular users and Machine-Type Devices (MTDs). The aggregation of the locally generated packets is performed by a user device, which aggregates the machine-type data, supplements it with its own data and transmits all of them to the Base Station. We observe a fundamental trade-off between latency and the transmit power needed to deliver the aggregate traffic, in a sense that lower latency requires increase in the transmit power.

Connected through hyperedge HE-1 (induced by source paper "self organized scheduling request for uplink 5g networks a d2d clustering approach"):
  with members:
    - "power control and channel allocation for d2d underlaid cellular networks"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "sinr and throughput of dense cellular networks with stretched exponential path loss"
    - "modeling and analysis of cellular networks using stochastic geometry a tutorial"
    - "the meta distribution of the sir for cellular networks with power control"
    - "stochastic geometry modeling and analysis of single and multi cluster wireless networks"
    - "spatiotemporal stochastic modeling of iot enabled cellular networks scalability and stability analysis"
    - "analytical modeling of interference aware power control for the uplink of heterogeneous cellular networks"

Connected through hyperedge HE-2 (induced by source paper "massive machine type communication with data aggregation and resource scheduling"):
  with members:
    - "massive multiple access based on superposition raptor codes for cellular m2m communications"
    - "massive non orthogonal multiple access for cellular iot potentials and limitations"
    - "spatiotemporal stochastic modeling of iot enabled cellular networks scalability and stability analysis"
    - "energy efficient scheduling and grouping for machine type communications over cellular networks"
    - "a delay optimal mac and packet scheduler for heterogeneous m2m uplink"
    - "e 2 mac energy efficient medium access for massive m2m communications"
    - "massive machine type communications in 5g physical and mac layer solutions"
    - "random cell association and void probability in poisson distributed cellular networks"

Connected through hyperedge HE-3 (induced by source paper "energy efficient scheduling and grouping for machine type communications over cellular networks"):
  with members:
    - "e 2 mac energy efficient medium access for massive m2m communications"
    - "understanding the iot connectivity landscape a contemporary m2m radio technology roadmap"
    - "a survey on device to device communication in cellular networks"
    - "zero outage cellular downlink with fixed rate d2d underlay"
    - "reliable reporting for massive m2m communications with periodic resource pooling"
    - "power efficient system design for cellular based machine to machine communications"

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)