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: "massive autonomous uav path planning a neural network based mean field game theoretic approach"
  Abstract: This paper investigates the autonomous control of massive unmanned aerial vehicles (UAVs) for mission-critical applications (e.g., dispatching many UAVs from a source to a destination for firefighting). Achieving their fast travel and low motion energy without inter-UAV collision under wind perturbation is a daunting control task, which incurs huge communication energy for exchanging UAV states in real time. We tackle this problem by exploiting a mean-field game (MFG) theoretic control method that requires the UAV state exchanges only once at the initial source. Afterwards, each UAV can control its acceleration by locally solving two partial differential equations (PDEs), known as the Hamilton-Jacobi-Bellman (HJB) and Fokker-Planck-Kolmogorov (FPK) equations. This approach, however, brings about huge computation energy for solving the PDEs, particularly under multi-dimensional UAV states. We address this issue by utilizing a machine learning (ML) method where two separate ML models approximate the solutions of the HJB and FPK equations. These ML models are trained and exploited using an online gradient descent method with low computational complexity. Numerical evaluations validate that the proposed ML aided MFG theoretic algorithm, referred to as MFG learning control, is effective in collision avoidance with low communication energy and acceptable computation energy.

Connected through hyperedge HE-1 (induced by source paper "extreme urllc vision challenges and key enablers"):
  with members:
    - "ultra reliable and low latency vehicular communication an active learning approach"
    - "one pixel image and rf signal based split learning for mmwave received power prediction"
    - "time synchronization in 5g wireless edge requirements and solutions for critical mtc"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "wireless network intelligence at the edge"
    - "urllc embb slicing to support vr multimodal perceptions over wireless cellular systems"
    - "a statistical learning approach to ultra reliable low latency communication"

Connected through hyperedge HE-2 (induced by source paper "communication efficient massive uav online path control federated learning meets mean field game theory"):
  with members:
    - "remote uav online path planning via neural network based opportunistic control"
    - "reinforcement learning for uav attitude control"
    - "massive uav to ground communication and its stable movement control a mean field approach"
    - "autonomous uav navigation using reinforcement learning"
    - "virtualized control over fog interplay between reliability and latency"
    - "energy efficient uav communication with trajectory optimization"
    - "communication efficient learning of deep networks from decentralized data"

Connected through hyperedge HE-3 (induced by source paper "predictive control and communication co design a gaussian process regression approach"):
  with members:
    - "extreme urllc vision challenges and key enablers"
    - "taming the tail of maximal information age in wireless industrial networks"
    - "wireless network intelligence at the edge"
    - "joint communication and control for wireless autonomous vehicular platoon systems"
    - "wireless network design for control systems a survey"
    - "stability and capacity regions or discrete time queueing networks"

Connected through hyperedge HE-4 (induced by source paper "q gadmm quantized group admm for communication efficient decentralized machine learning"):
  with members:
    - "extreme urllc vision challenges and key enablers"
    - "communication efficient massive uav online path control federated learning meets mean field game theory"
    - "predictive control and communication co design a gaussian process regression approach"
    - "decentralized stochastic optimization and gossip algorithms with compressed communication"
    - "accelerating federated learning via momentum gradient descent"
    - "advances and open problems in federated learning"
    - "gadmm fast and communication efficient framework for distributed machine learning"
    - "distilling on device intelligence at the network edge"

Connected through hyperedge HE-5 (induced by source paper "remote uav online path planning via neural network based opportunistic control"):
  with members:
    - "wireless network intelligence at the edge"
    - "massive uav to ground communication and its stable movement control a mean field approach"
    - "autonomous uav navigation using reinforcement learning"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "efficient deployment of multiple unmanned aerial vehicles for optimal wireless coverage"

Connected through hyperedge HE-6 (induced by source paper "federated reinforcement distillation with proxy experience memory"):
  with members:
    - "communication efficient on device machine learning federated distillation and augmentation under non iid private data"
    - "asynchronous methods for deep reinforcement learning"
    - "communication efficient learning of deep networks from decentralized data"
    - "dueling network architectures for deep reinforcement learning"

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.SY(Systems and Control)