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: "near optimal sensor placement for linear inverse problems"
  Abstract: A classic problem is the estimation of a set of parameters from measurements collected by only a few sensors. The number of sensors is often limited by physical or economical constraints and their placement is of fundamental importance to obtain accurate estimates. Unfortunately, the selection of the optimal sensor locations is intrinsically combinatorial and the available approximation algorithms are not guaranteed to generate good solutions in all cases of interest. We propose FrameSense, a greedy algorithm for the selection of optimal sensor locations. The core cost function of the algorithm is the frame potential, a scalar property of matrices that measures the orthogonality of its rows. Notably, FrameSense is the first algorithm that is near-optimal in terms of mean square error, meaning that its solution is always guaranteed to be close to the optimal one. Moreover, we show with an extensive set of numerical experiments that FrameSense achieves state-of-the-art performance while having the lowest computational cost, when compared to other greedy methods.

Connected through hyperedge HE-1 (induced by source paper "greedy sampling of graph signals"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "stationary signal processing on graphs"
    - "stationary graph processes and spectral estimation"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "adaptive least mean squares estimation of graph signals"
    - "compressive spectral clustering"
    - "sampling of graph signals with successive local aggregations"
    - "signals on graphs uncertainty principle and sampling"

Connected through hyperedge HE-2 (induced by source paper "reconstructing gaussian sources by spatial sampling"):
  with members:
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "sampling rate distortion"
    - "lossy computing of correlated sources with fractional sampling"
    - "the sampling rate distortion tradeoff for sparsity pattern recovery in compressed sensing"
    - "distributed source coding in dense sensor networks"

Connected through hyperedge HE-3 (induced by source paper "space time sampling for network observability"):
  with members:
    - "deterministic and randomized actuator scheduling with guaranteed performance bounds"
    - "the observability radius of networks"
    - "a note on the sum of correlated gamma random variables"
    - "graph sparsification by effective resistances"

Connected through hyperedge HE-4 (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"
    - "sparsity promoting sensor selection for non linear measurement models"

Connected through hyperedge HE-5 (induced by source paper "insense incoherent sensor selection for sparse signals"):
  with members:
    - "projection onto the probability simplex an efficient algorithm with a simple proof and an application"
    - "sparsity promoting sensor selection for non linear measurement models"
    - "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-6 (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"
    - "sparsity promoting sensor selection for non linear measurement models"
    - "relay subset selection in wireless networks using partial decode and forward transmission"

Connected through hyperedge HE-7 (induced by source paper "sensor management with time energy and communication constraints"):
  with members:
    - "sensor placement by maximal projection on minimum eigenspace for linear inverse problems"
    - "efficient sampling set selection for bandlimited graph signals using graph spectral proxies"
    - "strategies for distributed sensor selection using convex optimization"

Connected through hyperedge HE-8 (induced by source paper "distributed active state estimation with user specified accuracy"):
  with members:
    - "decentralized control of partially observable markov decision processes using belief space macro actions"

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)