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: "owl a reliable online watcher for lte control channel measurements"
  Abstract: Reliable network measurements are a fundamental component of networking research as they enable network analysis, system debugging, performance evaluation and optimization. In particular, decoding the LTE control channel would give access to the full base station traffic at a 1 ms granularity, thus allowing for traffic profiling and accurate measurements. Although a few open-source implementations of LTE are available, they do not provide tools to reliably decoding the LTE control channel and, thus, accessing the scheduling information. In this paper, we present OWL, an Online Watcher for LTE that is able to decode all the resource blocks in more than 99% of the system frames, significantly outperforming existing non-commercial prior decoders. Compared to previous attempts, OWL grounds the decoding procedure on information obtained from the LTE random access mechanism. This makes it possible to run our software on inexpensive hardware coupled with almost any software defined radio capable of sampling the LTE signal with sufficient accuracy.

Connected through hyperedge HE-1 (induced by source paper "pbe cc congestion control via endpoint centric physical layer bandwidth measurements"):
  with members:
    - "abc a simple explicit congestion control protocol for wireless networks"
    - "fairness of congestion based congestion control experimental evaluation and analysis"
    - "srslte an open source platform for lte evolution and experimentation"
    - "pcc re architecting congestion control for consistent high performance"

Connected through hyperedge HE-2 (induced by source paper "data driven network simulation for performance analysis of anticipatory vehicular communication systems"):
  with members:
    - "towards 6g networks use cases and technologies"
    - "empirical analysis of client based network quality prediction in vehicular multi mno networks"
    - "system of systems modeling analysis and optimization of hybrid vehicular traffic"
    - "heterogeneous v2v communications in multi link and multi rat vehicular networks"
    - "boosting vehicle to cloud communication by machine learning enabled context prediction"
    - "toward intelligent vehicular networks a machine learning framework"
    - "machine learning based context predictive car to cloud communication using multi layer connectivity maps for upcoming 5g networks"
    - "deep learning in physical layer communications"

Connected through hyperedge HE-3 (induced by source paper "falcon an accurate real time monitor for client based mobile network data analytics"):
  with members:
    - "boosting vehicle to cloud communication by machine learning enabled context prediction"
    - "discover your competition in lte client based passive data rate prediction by machine learning"
    - "client based control channel analysis for connectivity estimation in lte networks"
    - "srslte an open source platform for lte evolution and experimentation"

Connected through hyperedge HE-4 (induced by source paper "from 4g to 5g self organized network management meets machine learning"):
  with members:
    - "knowledge defined networking"
    - "survey on network virtualization hypervisors for software defined networking"
    - "software defined networking a comprehensive survey"
    - "representation learning a review and new perspectives"
    - "popular ensemble methods an empirical study"

Connected through hyperedge HE-5 (induced by source paper "discover your competition in lte client based passive data rate prediction by machine learning"):
  with members:
    - "rushing full speed with lte advanced is economical a power consumption analysis"
    - "client based control channel analysis for connectivity estimation in lte networks"
    - "srslte an open source platform for lte evolution and experimentation"

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)