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: "hardware accelerated sdr platform for adaptive air interfaces"
  Abstract: The future 5G wireless infrastructure will support any-to-any connectivity between densely deployed smart objects that form the emerging paradigm known as the Internet of Everything (IoE). Compared to traditional wireless networks that enable communication between devices using a single technology, 5G networks will need to support seamless connectivity between heterogeneous wireless objects and IoE networks. To tackle the complexity and versatility of future IoE networks, 5G will need to guarantee optimal usage of both spectrum and energy resources and further support technology-agnostic connectivity between objects. One way to realize this is to combine intelligent network control with adaptive software defined air interfaces. In this paper, a flexible and compact platform is proposed for on-the-fly composition of low-power adaptive air interfaces, based on hardware/software co-processing. Compared to traditional Software Defined Radio (SDR) systems that perform computationally-intensive signal processing algorithms in software, consume significantly power and have a large form factor, the proposed platform uses modern hybrid FPGA technology combined with novel ideas such as RF Network-on-Chip (RFNoC) and partial reconfiguration. The resulting system enables composition of reconfigurable air interfaces based on hardware/software co-processing on a single chip, allowing high processing throughput, at a smaller form factor and reduced power consumption.

Connected through hyperedge HE-1 (induced by source paper "software defined radios architecture state of the art and challenges"):
  with members:
    - "network function virtualization state of the art and research challenges"
    - "a traffic load balancing framework for software defined radio access networks powered by hybrid energy sources"
    - "software defined radio architecture survey for cognitive testbeds"

Connected through hyperedge HE-2 (induced by source paper "end to end learning from spectrum data a deep learning approach for wireless signal identification in spectrum monitoring applications"):
  with members:
    - "electrosense open and big spectrum data"
    - "deep learning models for wireless signal classification with distributed low cost spectrum sensors"
    - "an introduction to deep learning for the physical layer"
    - "spectrum monitoring for radar bands using deep convolutional neural networks"
    - "wireless interference identification with convolutional neural networks"
    - "will sdn be part of 5g"
    - "deepsense a unified deep learning framework for time series mobile sensing data processing"
    - "demo wiscop wireless sensor communication prototyping platform"

Connected through hyperedge HE-3 (induced by source paper "will sdn be part of 5g"):
  with members:
    - "5g fronthaul latency and jitter studies of cpri over ethernet"
    - "r fft function split at ifft fft in unified lte cran and cable access network"
    - "nfv and sdn key technology enablers for 5g networks"
    - "srslte an open source platform for lte evolution and experimentation"
    - "software defined optical networks sdons a comprehensive survey"
    - "cross layer software defined 5g network"
    - "are heterogeneous cloud based radio access networks cost effective"
    - "fronthaul constrained cloud radio access networks insights and challenges"

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)