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: "operational rate distortion performance of single source and distributed compressed sensing"
  Abstract: We consider correlated and distributed sources without cooperation at the encoder. For these sources, we derive the best achievable performance in the rate-distortion sense of any distributed compressed sensing scheme, under the constraint of high-rate quantization. Moreover, under this model we derive a closed-form expression of the rate gain achieved by taking into account the correlation of the sources at the receiver and a closed-form expression of the average performance of the oracle receiver for independent and joint reconstruction. Finally, we show experimentally that the exploitation of the correlation between the sources performs close to optimal and that the only penalty is due to the missing knowledge of the sparsity support as in (non distributed) compressed sensing. Even if the derivation is performed in the large system regime, where signal and system parameters tend to infinity, numerical results show that the equations match simulations for parameter values of practical interest.

Connected through hyperedge HE-1 (induced by source paper "limits on sparse data acquisition ric analysis of finite gaussian matrices"):
  with members:
    - "d oamp a denoising based signal recovery algorithm for compressed sensing"
    - "graded quantization for multiple description coding of compressive measurements"
    - "refined analysis of sparse mimo radar"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "improved bounds on restricted isometry constants for gaussian matrices"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "compressed sensing how sharp is the restricted isometry property"

Connected through hyperedge HE-2 (induced by source paper "distributed coding of multiview sparse sources with joint recovery"):
  with members:
    - "sparse signal reconstruction with multiple side information using adaptive weights for multiview sources"
    - "compressed sensing with side information geometrical interpretation and performance bounds"

Connected through hyperedge HE-3 (induced by source paper "graded quantization for multiple description coding of compressive measurements"):
  with members:
    - "graded quantization democracy for multiple descriptions in compressed sensing"
    - "a parallel compressive imaging architecture for one shot acquisition"

Connected through hyperedge HE-4 (induced by source paper "exact performance analysis of the oracle receiver for compressed sensing reconstruction"):
  with members:
    - "the pros and cons of compressive sensing for wideband signal acquisition noise folding versus dynamic range"
    - "regime change bit depth versus measurement rate in compressive sensing"
    - "how well can we estimate a sparse vector"

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)