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: "on sensing capacity of sensor networks for the class of linear observation fixed snr models"
  Abstract: In this paper we address the problem of finding the sensing cap acity of sensor networks for a class of linear observation models and a fixed SNR regime. Sensing cap acity is defined as the maximum number of signal dimensions reliably identified per sensor observa tion. In this context sparsity of the phenomena is a key feature that determines sensing capacity. Precludi ng the SNR of the environment the effect of sparsity on the number of measurements required for accurate reconstruction of a sparse phenomena has been widely dealt with under compressed sensing. Nevertheless the development there was motivated from an algorithmic perspective. In this paper our aim is to derive these bounds in an information theoretic set-up and thus provide algorithm independent conditions for reliable reconstruction of sparse signals. In this direction we first generalize the Fano’s inequality a nd provide lower bounds to the probability of error in reconstruction subject to an arbitrary distorti on criteria. Using these lower bounds to the probability of error, we derive upper bounds to sensing capacity and show that for fixed SNR regime sensing capacity goes down to zero as sparsity goes down to zero. This means that disproportionately more sensors are required to monitor very sparse events. We derive lower bounds to sensing capacity (achievable) via deriving upper bounds to the probability of error via adaptation to a max-likelihood detection set-up under a given distortion criteria. These l ower bounds to sensing capacity exhibit similar behavior though there is an SNR gap in the upper and lower bounds. Subsequently, we show the effect of correlation in sensing across sensors and across sensing modalities on sensing capacity for various degrees and models of correlation. Our next main contribution is that we show the effect of sensing diversity on sensing capacity, an effect that has not been co nsidered before. Sensing diversity is related to the effective coverage of a sensor with respect to the field. In this direction we show the following results (a) Sensing capacity goes down as sensing diversity per sensor goes down; (b) Random sampling (coverage) of the field by sensors is better than contiguous l ocation sampling (coverage). In essence the

Connected through hyperedge HE-1 (induced by source paper "measurement bounds for sparse signal ensembles via graphical models"):
  with members:
    - "subspace methods for joint sparse recovery"
    - "theoretical and empirical results for recovery from multiple measurements"
    - "model based compressive sensing"
    - "rank awareness in joint sparse recovery"
    - "average case analysis of multichannel sparse recovery using convex relaxation"

Connected through hyperedge HE-2 (induced by source paper "bounds on the reconstruction of sparse signal ensembles from distributed measurements"):
  with members:
    - "compressive music a missing link between compressive sensing and array signal processing"
    - "theoretical and empirical results for recovery from multiple measurements"
    - "model based compressive sensing"
    - "rank awareness in joint sparse recovery"
    - "average case analysis of multichannel sparse recovery using convex relaxation"

Connected through hyperedge HE-3 (induced by source paper "the sensing capacity of sensor networks"):
  with members:
    - "sensing capacity for markov random fields"

Connected through hyperedge HE-4 (induced by source paper "the sampling rate distortion tradeoff for sparsity pattern recovery in compressed sensing"):
  with members:
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "approximate sparsity pattern recovery information theoretic lower bounds"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "estimation with random linear mixing belief propagation and compressed sensing"
    - "information theoretic bounds for compressed sensing"
    - "a typical reconstruction limit of compressed sensing based on lp norm minimization"
    - "necessary and sufficient conditions for sparsity pattern recovery"

Connected through hyperedge HE-5 (induced by source paper "approximate sparsity pattern recovery information theoretic lower bounds"):
  with members:
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "the sampling rate distortion tradeoff for sparsity pattern recovery in compressed sensing"
    - "information theoretic bounds for compressed sensing"
    - "number of measurements in sparse signal recovery"
    - "a typical reconstruction limit of compressed sensing based on lp norm minimization"
    - "necessary and sufficient conditions for sparsity pattern recovery"
    - "shannon theoretic limits on noisy compressive sampling"
    - "randomly spread cdma asymptotics via statistical physics"

Connected through hyperedge HE-6 (induced by source paper "compressed compressed sensing"):
  with members:
    - "approximate sparsity pattern recovery information theoretic lower bounds"
    - "information theoretic bounds for compressed sensing"
    - "necessary and sufficient conditions for sparsity pattern recovery"
    - "shannon theoretic limits on noisy compressive sampling"

Connected through hyperedge HE-7 (induced by source paper "necessary and sufficient conditions for sparsity pattern recovery"):
  with members:
    - "on off random access channels a compressed sensing framework"
    - "shannon theoretic limits on noisy compressive sampling"

Connected through hyperedge HE-8 (induced by source paper "distributed compressive sensing"):
  with members:
    - "model based compressive sensing"

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.IT(Information Theory)