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: "information acquisition and exploitation in multichannel wireless networks"
  Abstract: A wireless system with multiple channels is considered, where each channel has several transmission states. A user learns about the instantaneous state of an available channel by transmitting a control packet in it. Since probing all channels consumes significant energy and time, a user needs to determine what and how much information it needs to acquire about the instantaneous states of the available channels so that it can maximize its transmission rate. This motivates the study of the trade-off between the cost of information acquisition and its value towards improving the transmission rate. #R##N#A simple model is presented for studying this information acquisition and exploitation trade-off when the channels are multi-state, with different distributions and information acquisition costs. The objective is to maximize a utility function which depends on both the cost and value of information. Solution techniques are presented for computing near-optimal policies with succinct representation in polynomial time. These policies provably achieve at least a fixed constant factor of the optimal utility on any problem instance, and in addition, have natural characterizations. The techniques are based on exploiting the structure of the optimal policy, and use of Lagrangean relaxations which simplify the space of approximately optimal solutions.

Connected through hyperedge HE-1 (induced by source paper "approximation algorithms for optimal decision trees and adaptive tsp problems"):
  with members:
    - "when lp is the cure for your matching woes improved bounds for stochastic matchings"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"
    - "average case active learning with costs"

Connected through hyperedge HE-2 (induced by source paper "adaptive uncertainty resolution in bayesian combinatorial optimization problems"):
  with members:
    - "near optimal nonmyopic value of information in graphical models"
    - "sequential design of experiments via linear programming"
    - "least expected cost query optimization an exercise in utility"

Connected through hyperedge HE-3 (induced by source paper "approximation algorithms for restless bandit problems"):
  with members:
    - "optimality of myopic sensing in multi channel opportunistic access"
    - "on myopic sensing for multi channel opportunistic access structure optimality and performance"
    - "indexability of restless bandit problems and optimality of whittle s index for dynamic multichannel access"
    - "online convex optimization in the bandit setting gradient descent without a gradient"
    - "the k server dual and loose competitiveness for paging"

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)