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: "asynchronous stochastic approximation based learning algorithms for as you go deployment of wireless relay networks along a line"
  Abstract: We are motivated by the need for impromptu (or as-you-go) deployment of multihop wireless networks, by human agents or robots; the agent moves along a line, makes wireless link quality measurements at regular intervals, and makes on-line placement decisions using these measurements. As a first step, we have formulated such deployment along a line as a sequential decision problem. In our earlier work, we proposed two possible deployment approaches: (i) the pure as-you-go approach where the deployment agent can only move forward, and (ii) the explore-forward approach where the deployment agent explores a few successive steps and then selects the best relay placement location. The latter was shown to provide better performance but at the expense of more measurements and deployment time, which makes explore-forward impractical for quick deployment by an energy constrained agent such as a UAV. Further, the deployment algorithm should not require prior knowledge of the parameters of the wireless propagation model. In [1] we, therefore, developed learning algorithms for the explore-forward approach. #R##N#The current paper provides deploy-and-learn algorithms for the pure as-you-go approach. We formulate the sequential relay deployment problem as an average cost Markov decision process (MDP), which trades off among power consumption, link outage probabilities, and the number of deployed relay nodes. First we show structural results for the optimal policy. Next, by exploiting the special structure of the optimality equation and by using the theory of asynchronous stochastic approximation, we develop two learning algorithms that asymptotically converge to the set of optimal policies as deployment progresses. Numerical results show reasonably fast speed of convergence, and hence the model-free algorithms can be useful for practical, fast deployment of emergency wireless networks.

Connected through hyperedge HE-1 (induced by source paper "uav positioning and power control for two way wireless relaying"):
  with members:
    - "comp in the sky uav placement and movement optimization for multi user communications"
    - "local map assisted positioning for flying wireless relays"
    - "a framework for one bit and constant envelope precoding over multiuser massive miso channels"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "common throughput maximization in uav enabled ofdma systems with delay consideration"
    - "placement optimization of uav mounted mobile base stations"
    - "joint trajectory and communication design for multi uav enabled wireless networks"
    - "joint altitude and beamwidth optimization for uav enabled multiuser communications"

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)