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: "set k cover algorithms for energy efficient monitoring in wireless sensor networks"
  Abstract: Wireless sensor networks (WSNs) are emerging as an effective means for environment monitoring. This paper investigates a strategy for energy efficient monitoring in WSNs that partitions the sensors into covers, and then activates the covers iteratively in a round-robin fashion. This approach takes advantage of the overlap created when many sensors monitor a single area. Our work builds upon previous work in "Power Efficient Organization of Wireless Sensor Networks" by Slijepcevic and Potkonjak, where the model is first formulated. We have designed three approximation algorithms for a variation of the SET K-COVER problem, where the objective is to partition the sensors into covers such that the number of covers that include an area, summed over all areas, is maximized. The first algorithm is randomized and partitions the sensors, in expectation, within a fraction 1 - 1/e (~.63) of the optimum. We present two other deterministic approximation algorithms. One is a distributed greedy algorithm with a 1/2 approximation ratio and the other is a centralized greedy algorithm with a 1 - 1/e approximation ratio. We show that it is NP-Complete to guarantee better than 15/16 of the optimal coverage, indicating that all three algorithms perform well with respect to the best approximation algorithm possible. Simulations indicate that in practice, the deterministic algorithms perform far above their worst case bounds, consistently covering more than 72% of what is covered by an optimum solution. Simulations also indicate that the increase in longevity is proportional to the amount of overlap amongst the sensors. The algorithms are fast, easy to use, and according to simulations, significantly increase the longevity of sensor networks. The randomized algorithm in particular seems quite practical.

Connected through hyperedge HE-1 (induced by source paper "scheduling resource bounded monitoring devices for event detection and isolation in networks"):
  with members:
    - "sensor placement for fault location identification in water networks a minimum test cover approach"
    - "detection and isolation of failures in directed networks of lti systems"

Connected through hyperedge HE-2 (induced by source paper "set it and forget it approximating the set once strip cover problem"):
  with members:
    - "maximizing barrier coverage lifetime with mobile sensors"
    - "connecting a set of circles with minimum sum of radii"
    - "decomposing coverings and the planar sensor cover problem"
    - "decomposition of multiple coverings into more parts"
    - "restricted strip covering and the sensor cover problem"

Connected through hyperedge HE-3 (induced by source paper "online distributed sensor selection"):
  with members:
    - "near optimal nonmyopic value of information in graphical models"

Connected through hyperedge HE-4 (induced by source paper "optimal value of information in graphical models"):
  with members:
    - "tractable inference for complex stochastic processes"
    - "inference in hybrid networks theoretical limits and practical algorithms"
    - "an approximate nonmyopic computation for value of information"
    - "learning diagnostic policies from examples by systematic search"
    - "near optimal nonmyopic value of information in graphical models"
    - "complexity results and approximation strategies for map explanations"
    - "the computational complexity of probabilistic planning"
    - "active learning with statistical models"

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.DS(Data Structures and Algorithms)