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: "big spectrum data the new resource for cognitive wireless networking"
  Abstract: AbstractThe era of Big Data is here now, which has brought both unprecedented opportunities and criticalchallenges. In this article, from a perspective of cognitive wireless networking, we start with a deﬁnitionof Big Spectrum Data by analyzing its characteristics in terms of six V’s, i.e., volume, variety, velocity,veracity, viability, and value. We then present a high-level tutorial on research frontiers in Big SpectrumData analytics to guide the development of practical algorithms. We also highlight Big Spectrum Dataas the new resource for cognitive wireless networking by presenting the emerging use cases. We now live in an era of data deluge. We live in a world that has more than a billion transistorsper human; a world with more than 4 billion mobile phone subscribers and about 30 billion radiofrequency identiﬁcation tags produced globally within the last two years [1]. All these sensorsgenerate data. Sadly, much of this data is simply thrown away, because of the lack of efﬁcientmechanisms to derive value from it. This fact motivates the worldwide increasing interests inBig Data.Although there are still debates on whether Big Data is a big opportunity or a big bubble, BigData is here now and is going to transform how we gain insights and how we make decisionsin the future [2]. To leverage the Big Data opportunities and challenges, many governments,organizations and academic institutions come forward to take initiates. For example, the US

Connected through hyperedge HE-1 (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"
    - "hardware accelerated sdr platform for adaptive air interfaces"
    - "will sdn be part of 5g"
    - "deepsense a unified deep learning framework for time series mobile sensing data processing"

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.OH(Other Computer Science)