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: "sparsity promoting sensor selection for non linear measurement models"
  Abstract: Sensor selection is an important design problem in large-scale sensor networks. Sensor selection can be interpreted as the problem of selecting the best subset of sensors that guarantees a certain estimation performance. We focus on observations that are related to a general non-linear model. The proposed framework is valid as long as the observations are independent, and its likelihood satisfies the regularity conditions. We use several functions of the Cram\'er-Rao bound (CRB) as a performance measure. We formulate the sensor selection problem as the design of a selection vector, which in its original form is a nonconvex l0-(quasi) norm optimization problem. We present relaxed sensor selection solvers that can be efficiently solved in polynomial time. We also propose a projected subgradient algorithm that is attractive for large-scale problems and also show how the algorithm can be easily distributed. The proposed framework is illustrated with a number of examples related to sensor placement design for localization.

Connected through hyperedge HE-1 (induced by source paper "graph sampling for covariance estimation"):
  with members:
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"
    - "learning sparse graphs under smoothness prior"
    - "subsampling for graph power spectrum estimation"
    - "uncertainty principle and sampling of signals defined on graphs"
    - "discrete signal processing on graphs sampling theory"
    - "gspbox a toolbox for signal processing on graphs"
    - "near optimal sensor placement for linear inverse problems"

Connected through hyperedge HE-2 (induced by source paper "insense incoherent sensor selection for sparse signals"):
  with members:
    - "near optimal sensor placement for linear inverse problems"
    - "projection onto the probability simplex an efficient algorithm with a simple proof and an application"
    - "exact recovery conditions for sparse representations with partial support information"
    - "online distributed sensor selection"
    - "deterministic construction of binary bipolar and ternary compressed sensing matrices"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "consistent sensor relay and link selection in wireless sensor networks"):
  with members:
    - "a class of prediction correction methods for time varying convex optimization"
    - "qos constrained optimal sink and relay placement in planned wireless sensor networks"
    - "near optimal sensor placement for linear inverse problems"
    - "relay subset selection in wireless networks using partial decode and forward transmission"

Connected through hyperedge HE-4 (induced by source paper "microphone subset selection for mvdr beamformer based noise reduction"):
  with members:
    - "online distributed sensor selection"

Connected through hyperedge HE-5 (induced by source paper "sensor selection and power allocation strategies for energy harvesting wireless sensor networks"):
  with members:
    - "energy harvesting wireless communications a review of recent advances"
    - "source channel coding under energy delay and buffer constraints"
    - "energy harvesting broadband communication systems with processing energy cost"
    - "throughput maximization for the gaussian relay channel with energy harvesting constraints"
    - "sum rate optimal power policies for energy harvesting transmitters in an interference channel"
    - "optimal energy allocation for wireless communications with energy harvesting constraints"
    - "a general framework for the optimization of energy harvesting communication systems with battery imperfections"
    - "transmission with energy harvesting nodes in fading wireless channels optimal policies"

Connected through hyperedge HE-6 (induced by source paper "subsampling for graph power spectrum estimation"):
  with members:
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"
    - "sampling of graph signals with successive local aggregations"
    - "uncertainty principle and sampling of signals defined on graphs"
    - "gspbox a toolbox for signal processing on graphs"
    - "near optimal sensor placement for linear inverse problems"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "discrete signal processing on graphs"

Connected through hyperedge HE-7 (induced by source paper "sensor placement by maximal projection on minimum eigenspace for linear inverse problems"):
  with members:
    - "near optimal sensor placement for linear inverse problems"
    - "sparsity exploiting anchor placement for localization in 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.IT(Information Theory)