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: "b a t mobile leveraging mobility control knowledge for efficient routing in mobile robotic networks"
  Abstract: Efficient routing is one of the key challenges of wireless networking for unmanned autonomous vehicles (UAVs) due to dynamically changing channel and network topology characteristics. Various well known mobile-ad-hoc routing protocols, such as AODV, OLSR and B.A.T.M.A.N. have been proposed to allow for proactive and reactive routing decisions. In this paper, we present a novel approach which leverages application layer knowledge derived from mobility control algorithms guiding the behavior of UAVs to fulfill a dedicated task. Thereby a prediction of future trajectories of the UAVs can be integrated with the routing protocol to avoid unexpected route breaks and packet loss. The proposed extension of the B.A.T.M.A.N. routing protocol by a mobility prediction component - called B.A.T.Mobile - has shown to be very effective to realize this concept. The results of in-depth simulation studies show that the proposed protocol reaches a distinct higher availability compared to the established approaches and shows robust behavior even in challenging channel conditions.

Connected through hyperedge HE-1 (induced by source paper "lightweight simulation of hybrid aerial and ground based vehicular communication networks"):
  with members:
    - "system of systems modeling analysis and optimization of hybrid vehicular traffic"
    - "boosting vehicle to cloud communication by machine learning enabled context prediction"
    - "performance analysis of c v2x mode 4 communication introducing an open source c v2x simulator"
    - "performance evaluation and optimization of b a t m a n v routing for aerial and ground based mobile ad hoc networks"
    - "machine learning based context predictive car to cloud communication using multi layer connectivity maps for upcoming 5g networks"
    - "flynetsim an open source synchronized uav network simulator based on ns 3 and ardupilot"
    - "efficient machine type communication using multi metric context awareness for cars used as mobile sensors in upcoming 5g networks"
    - "machine learning based uplink transmission power prediction for lte and upcoming 5g networks using passive downlink indicators"

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"
    - "discover your competition in lte client based passive data rate prediction by machine learning"
    - "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"
    - "kernel based adaptive online reconstruction of coverage maps with side information"

Connected through hyperedge HE-3 (induced by source paper "unmanned aerial vehicles in logistics efficiency gains and communication performance of hybrid combinations of ground and aerial vehicles"):
  with members:
    - "lightweight simulation of hybrid aerial and ground based vehicular communication networks"
    - "system of systems modeling analysis and optimization of hybrid vehicular traffic"
    - "boosting vehicle to cloud communication by machine learning enabled context prediction"
    - "uav communications for 5g and beyond recent advances and future trends"
    - "performance analysis of c v2x mode 4 communication introducing an open source c v2x simulator"
    - "the sky is not the limit lte for unmanned aerial vehicles"
    - "lightweight joint simulation of vehicular mobility and communication with limosim"
    - "a survey of anticipatory mobile networking context based classification prediction methodologies and optimization techniques"

Connected through hyperedge HE-4 (induced by source paper "towards data driven simulation of end to end network performance indicators"):
  with members:
    - "empirical analysis of client based network quality prediction in vehicular multi mno networks"
    - "system of systems modeling analysis and optimization of hybrid vehicular traffic"
    - "boosting vehicle to cloud communication by machine learning enabled context prediction"
    - "machine learning based context predictive car to cloud communication using multi layer connectivity maps for upcoming 5g networks"
    - "efficient machine type communication using multi metric context awareness for cars used as mobile sensors in upcoming 5g networks"
    - "end to end learning of communications systems without a channel model"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "a survey of anticipatory mobile networking context based classification prediction methodologies and optimization techniques"

Connected through hyperedge HE-5 (induced by source paper "empirical analysis of client based network quality prediction in vehicular multi mno networks"):
  with members:
    - "system of systems modeling analysis and optimization of hybrid vehicular traffic"
    - "boosting vehicle to cloud communication by machine learning enabled context prediction"
    - "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"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "system of systems modeling analysis and optimization of hybrid vehicular traffic"):
  with members:
    - "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"
    - "lightweight joint simulation of vehicular mobility and communication with limosim"
    - "connected vehicular transportation data analytics and traffic dependent networking"
    - "a survey of anticipatory mobile networking context based classification prediction methodologies and optimization techniques"

Connected through hyperedge HE-7 (induced by source paper "performance evaluation and optimization of b a t m a n v routing for aerial and ground based mobile ad hoc networks"):
  with members:
    - "machine learning based context predictive car to cloud communication using multi layer connectivity maps for upcoming 5g networks"
    - "lightweight joint simulation of vehicular mobility and communication with limosim"
    - "a simple scheme for distributed passive load balancing in mobile ad hoc networks"
    - "a survey of anticipatory mobile networking context based classification prediction methodologies and optimization techniques"

Connected through hyperedge HE-8 (induced by source paper "robotic wireless sensor networks"):
  with members:
    - "heat diffusion pareto optimal dynamic routing for time varying wireless networks"
    - "empirical evaluation of the heat diffusion collection protocol for wireless sensor networks"
    - "towards controllability of wireless network quality using mobile robotic routers"
    - "interference power bound analysis of a network of wireless robots"
    - "sequential decision algorithms for measurement based impromptu deployment of a wireless relay network along a line"
    - "route swarm wireless network optimization through mobility"
    - "robotic message ferrying for wireless networks using coarse grained backpressure control"
    - "backpressure with adaptive redundancy bwar"

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)