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: "rogue drone detection a machine learning approach"
  Abstract: The emerging, practical and observed issue of how to detect rogue drones that carry terrestrial user equipment (UEs) on mobile networks is addressed in this paper. This issue has drawn much attention since the rogue drones may generate excessive interference to mobile networks and may not be allowed by regulations in some regions. In this paper, we propose a novel machine learning approach to identify the rogue drones in mobile networks based on radio measurements. We apply two classification machine learning models, Logistic Regression, and Decision Tree, using features from radio measurements to identify the rogue drones. We find that for high altitudes the proposed machine learning solutions can yield high rogue drone detection rate while not mis-classifying regular ground based UEs as rogue drone UEs. The detection accuracy however degrades at low altitudes.

Connected through hyperedge HE-1 (induced by source paper "when machine learning meets wireless cellular networks deployment challenges and applications"):
  with members:
    - "deepcabac context adaptive binary arithmetic coding for deep neural network compression"
    - "human centered artificial intelligence and machine learning"
    - "machine learning in the air"
    - "ofdm autoencoder for end to end learning of communications systems"
    - "scalable agent alignment via reward modeling a research direction"
    - "explainable artificial intelligence understanding visualizing and interpreting deep learning models"
    - "artificial neural networks based machine learning for wireless networks a tutorial"
    - "cell free massive mimo versus small cells"

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)