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: "application of machine learning for channel based message authentication in mission critical machine type communication"
  Abstract: The design of robust wireless communication systems for industrial applications such as closed loop control processes has been considered manifold recently. Additionally, the ongoing advances in the area of connected mobility have similar or even higher requirements regarding system reliability and availability. Beside unfulfilled reliability requirements, the availability of a system can further be reduced, if it is under attack in the sense of violation of information security goals such as data authenticity or integrity. In order to guarantee the safe operation of an application, a system has at least to be able to detect these attacks. Though there are numerous techniques in the sense of conventional cryptography in order to achieve that goal, these are not always suited for the requirements of the applications mentioned due to resource inefficiency. In the present work, we show how the goal of message authenticity based on physical layer security (PHYSEC) can be achieved. The main idea for such techniques is to exploit user specific characteristics of the wireless channel, especially in spatial domain. Additionally, we show the performance of our machine learning based approach and compare it with other existing approaches.

Connected through hyperedge HE-1 (induced by source paper "performance of statistical and machine learning techniques for physical layer authentication"):
  with members:
    - "machine learning for intelligent authentication in 5g and beyond wireless networks"
    - "statistical and machine learning based decision techniques for physical layer authentication"
    - "learning aided physical layer authentication as an intelligent process"
    - "analysis of channel based user authentication by key less and key based approaches"
    - "physical layer security in heterogeneous cellular networks"
    - "using the physical layer for wireless authentication in time variant channels"

Connected through hyperedge HE-2 (induced by source paper "statistical and machine learning based decision techniques for physical layer authentication"):
  with members:
    - "learning aided physical layer authentication as an intelligent process"
    - "analysis of channel based user authentication by key less and key based approaches"
    - "using the physical layer for wireless authentication in time variant channels"

Connected through hyperedge HE-3 (induced by source paper "physical layer authentication in mission critical mtc networks a security and delay performance analysis"):
  with members:
    - "channel impulse response based distributed physical layer authentication"
    - "physical layer authentication for mission critical machine type communication using gaussian mixture model based clustering"
    - "secure transmission of delay sensitive data over wireless fading channels"
    - "fingerprints in the ether using the physical layer for wireless authentication"
    - "using the physical layer for wireless authentication in time variant channels"

Connected through hyperedge HE-4 (induced by source paper "a console grid leveraged authentication and key agreement mechanism for lte sae"):
  with members:
    - "deep learning based dynamic watermarking for secure signal authentication in the internet of things"
    - "a secure and authenticated key management protocol sa kmp for vehicular networks"

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)