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: "sequential decision algorithms for measurement based impromptu deployment of a wireless relay network along a line"
  Abstract: We are motivated by the need, in some applications, for impromptu or as-you-go deployment of wireless sensor networks. A person walks along a line, starting from a sink node (e.g., a base-station), and proceeds towards a source node (e.g., a sensor) which is at an a priori unknown location. At equally spaced locations, he makes link quality measurements to the previous relay, and deploys relays at some of these locations, with the aim to connect the source to the sink by a multihop wireless path. In this paper, we consider two approaches for impromptu deployment: (i) the deployment agent can only move forward (which we call a pure as-you-go approach), and (ii) the deployment agent can make measurements over several consecutive steps before selecting a placement location among them (which we call an explore-forward approach). We consider a light traffic regime, and formulate the problem as a Markov decision process, where the trade-off is among the power used by the nodes, the outage probabilities in the links, and the number of relays placed per unit distance. We obtain the structures of the optimal policies for the pure as-you-go approach as well as for the explore-forward approach. We also consider natural heuristic algorithms, for comparison. Numerical examples show that the explore-forward approach significantly outperforms the pure as-you-go approach. Next, we propose two learning algorithms for the explore-forward approach, based on Stochastic Approximation, which asymptotically converge to the set of optimal policies, without using any knowledge of the radio propagation model. We demonstrate numerically that the learning algorithms can converge (as deployment progresses) to the set of optimal policies reasonably fast and, hence, can be practical, model-free algorithms for deployment over large regions.

Connected through hyperedge HE-1 (induced by source paper "security against false data injection attack in cyber physical systems"):
  with members:
    - "coding schemes for securing cyber physical systems against stealthy data injection attacks"

Connected through hyperedge HE-2 (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"
    - "b a t mobile leveraging mobility control knowledge for efficient routing in mobile robotic networks"
    - "towards controllability of wireless network quality using mobile robotic routers"
    - "interference power bound analysis of a network of wireless robots"
    - "route swarm wireless network optimization through mobility"
    - "robotic message ferrying for wireless networks using coarse grained backpressure control"
    - "backpressure with adaptive redundancy bwar"

Connected through hyperedge HE-3 (induced by source paper "asynchronous stochastic approximation based learning algorithms for as you go deployment of wireless relay networks along a line"):
  with members:
    - "deploy as you go wireless relay placement an optimal sequential decision approach using the multi relay channel model"
    - "impromptu deployment of wireless relay networks experiences along a forest trail"
    - "measurement based impromptu deployment of a multi hop wireless relay network"
    - "qos aware and survivable network design for planned wireless sensor networks"

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)