human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "an active sequential xampling detector for spectrum sensing"):
  Abstract: Enabling low power wireless devices to adopt Nyquist sampling at high carriers is prohibitive. In spectrum sensing, this limit calls for an analog front-end that can sweep different bands quickly, in order to use the available spectrum opportunistically. In this paper we propose a new framework that allows to optimize a sub-Nyquist sampling front-end to combine the benefits of optimum sequential sensing with those of Compressive Spectrum Sensing (CSS). The sensing strategy we propose is formulated as a sequential utility optimization problem. The utility function decreases linearly with the number of measurements (accrued to make a decision) and increases as a function of the signal components binary states. The optimization selects the best linear combinations of sub-bands to mix, in order to accrue maximum utility. The structure of the utility represents the trade-off between exploration, exploitation and risk of making an error, that is characteristic of the spectrum sensing problem. We first present the analog front-end architecture, and then map the measurement model into the abstract optimization problem proposed, and analyzed, in the remainder of the paper. We characterize the optimal policy under constraints on the sensing matrix and derive the approximation factor of the greedy approach. Numerical simulations showcase the benefits of combining opportunistic spectrum sensing with sub-Nyquist sampling.

Includes members:
  Member "utility maximizing sequential sensing over a finite horizon":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "active anomaly detection in heterogeneous processes")
  Member "converse bounds for noisy group testing with arbitrary measurement matrices":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "group testing an information theory perspective")
  Member "cross layer estimation and control for cognitive radio exploiting sparse network dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "thirty years of machine learning the road to pareto optimal next generation wireless networks")
  Member "info greedy sequential adaptive compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adaptivity provably helps information theoretic limits on l_0 cost of non adaptive sensing")
  Member "sub nyquist sampling for power spectrum sensing in cognitive radios a unified approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fast compressed power spectrum estimation towards a practical solution for wideband spectrum sensing")
  Member "wideband spectrum sensing with sub nyquist sampling in cognitive radios":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fast compressed power spectrum estimation towards a practical solution for wideband spectrum sensing")
  Member "boolean compressed sensing and noisy group testing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "serial quantization for sparse time sequences")
  Member "on optimality of myopic policy for restless multi armed bandit problem an axiomatic approach":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "minimax optimal algorithms for adversarial bandit problem with multiple plays")

Question: Please tell me which class the source paper 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.IT(Information Theory)