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: "discover your competition in lte client based passive data rate prediction by machine learning"
  Abstract: To receive the highest possible data rate or/and the most reliable connection, the User Equipment (UE) may want to choose between different networks. However, current LTE and LTE-Advanced mobile networks do not supply the UE with an explicit indicator about the currently achievable data rate. For this reason, the mobile device will only see what it obtains from the network once it actively sends data. A passive estimation in advance is therefore not doable without further effort. Although the device can identify its current radio conditions based on the received signal strength and quality, it has no information about the cell's traffic load caused by other users. To close this gap we present an Enhanced Client-based Control-Channel Analysis for Connectivity Estimation (E-C3ACE), which uncovers the cell load broken down by each single user. Based on this information and in conjunction with existing indicators like Reference Signal Received Power (RSRP) and Reference Signal Received Quality (RSRQ), a neural network is trained to perform a data rate prediction for the current LTE link. Compared to an earlier work, our approach reduces the average prediction error below one third. Applied in public networks, the predicted data rate differs by less than 1.5 Mbit/s in 93% of cases.

Connected through hyperedge HE-1 (induced by source paper "towards cooperative data rate prediction for future mobile and vehicular 6g networks"):
  with members:
    - "empirical analysis of client based network quality prediction in vehicular multi mno networks"
    - "data driven network simulation for performance analysis of anticipatory vehicular communication systems"
    - "boosting vehicle to cloud communication by machine learning enabled context prediction"
    - "falcon an accurate real time monitor for client based mobile network data analytics"
    - "wireless network intelligence at the edge"
    - "a survey of anticipatory mobile networking context based classification prediction methodologies and optimization techniques"

Connected through hyperedge HE-2 (induced by source paper "boosting vehicle to cloud communication by machine learning enabled context prediction"):
  with members:
    - "empirical analysis of client based network quality prediction in vehicular multi mno networks"
    - "machine learning based context predictive car to cloud communication using multi layer connectivity maps for upcoming 5g networks"
    - "machine learning based uplink transmission power prediction for lte and upcoming 5g networks using passive downlink indicators"
    - "connected vehicular transportation data analytics and traffic dependent networking"
    - "rushing full speed with lte advanced is economical a power consumption analysis"
    - "energy efficient uav communication with trajectory optimization"
    - "b a t mobile leveraging mobility control knowledge for efficient routing in mobile robotic networks"
    - "kernel based adaptive online reconstruction of coverage maps with side information"

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"
    - "owl a reliable online watcher for lte control channel measurements"
    - "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 "towards resilient 5g lessons learned from experimental evaluations of lte uplink jamming"):
  with members:
    - "lte phy layer vulnerability analysis and testing using open source sdr tools"
    - "easy 4g lte imsi catchers for non programmers"
    - "srslte an open source platform for lte evolution and experimentation"

Connected through hyperedge HE-5 (induced by source paper "machine learning based context predictive car to cloud communication using multi layer connectivity maps for upcoming 5g networks"):
  with members:
    - "machine learning based uplink transmission power prediction for lte and upcoming 5g networks using passive downlink indicators"
    - "connected vehicular transportation data analytics and traffic dependent networking"
    - "kernel based adaptive online reconstruction of coverage maps with side information"

Connected through hyperedge HE-6 (induced by source paper "machine learning based uplink transmission power prediction for lte and upcoming 5g networks using passive downlink indicators"):
  with members:
    - "machine learning based context predictive car to cloud communication using multi layer connectivity maps for upcoming 5g networks"
    - "srslte an open source platform for lte evolution and experimentation"
    - "a survey of anticipatory mobile networking context based classification prediction methodologies and optimization techniques"

Connected through hyperedge HE-7 (induced by source paper "efficient machine type communication using multi metric context awareness for cars used as mobile sensors in upcoming 5g networks"):
  with members:
    - "kernel based adaptive online reconstruction of coverage maps with side information"
    - "a survey of anticipatory mobile networking context based classification prediction methodologies and optimization techniques"

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)