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: "end to end learning of proactive handover policy for camera assisted mmwave networks using deep reinforcement learning"
  Abstract: For mmWave networks, this paper proposes an image-to-decision proactive handover framework, which directly maps camera images to a handover decision. With the help of camera images, the proposed framework enables a proactive handover, i.e., a handover is triggered before a temporal variation in the received power induced by obstacles even if the variation is extremely rapid such that it cannot be predicted from a time series of received power. Furthermore, direct mapping allows scalability for the number of obstacles. This paper shows that optimal mapping is learned via deep reinforcement learning (RL) by proving that the decision process in our proposed framework is a Markov decision process. While performing deep RL, this paper designs a neural network (NN) architecture for a network controller to successfully learn the use of lower-dimensional observations in state information and higher-dimensional image observations. The evaluations based on experimentally obtained camera images and received powers indicate that the learned handover policy in the proposed framework outperforms the learned policy in a received power-based handover framework.

Connected through hyperedge HE-1 (induced by source paper "cooperative sensing in deep rl based image to decision proactive handover for mmwave networks"):
  with members:
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "an mdp model for optimal handover decisions in mmwave cellular networks"
    - "depth map prediction from a single image using a multi scale deep network"

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)