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: "compressive music with optimized partial support for joint sparse recovery"
  Abstract: Multiple measurement vector (MMV) problem addresses the identification of unknown input vectors that share common sparse support. The MMV problems had been traditionally addressed either by sensor array signal processing or compressive sensing. However, recent breakthrough in this area such as compressive MUSIC (CS-MUSIC) or subspace-augumented MUSIC (SA-MUSIC) optimally combines the compressive sensing (CS) and array signal processing such that $k-r$ supports are first found by CS and the remaining $r$ supports are determined by generalized MUSIC criterion, where $k$ and $r$ denote the sparsity and the independent snapshots, respectively. Even though such hybrid approach significantly outperforms the conventional algorithms, its performance heavily depends on the correct identification of $k-r$ partial support by compressive sensing step, which often deteriorate the overall performance. The main contribution of this paper is, therefore, to show that as long as $k-r+1$ correct supports are included in any $k$-sparse CS solution, the optimal $k-r$ partial support can be found using a subspace fitting criterion, significantly improving the overall performance of CS-MUSIC. Furthermore, unlike the single measurement CS counterpart that requires infinite SNR for a perfect support recovery, we can derive an information theoretic sufficient condition for the perfect recovery using CS-MUSIC under a {\em finite} SNR scenario.

Connected through hyperedge HE-1 (induced by source paper "enhanced joint sparsity via iterative support detection"):
  with members:
    - "joint sparse recovery with semisupervised music"
    - "multi task sparse structure learning"
    - "omp based joint sparsity pattern recovery under communication constraints"
    - "exact joint sparse frequency recovery via optimization methods"
    - "an alternating direction algorithm for matrix completion with nonnegative factors"
    - "improving noise robustness in subspace based joint sparse recovery"
    - "subspace methods for joint sparse recovery"
    - "joint sparsity with different measurement matrices"

Connected through hyperedge HE-2 (induced by source paper "improving noise robustness in subspace based joint sparse recovery"):
  with members:
    - "subspace methods for joint sparse recovery"
    - "theoretical and empirical results for recovery from multiple measurements"
    - "rank awareness in joint sparse recovery"
    - "necessary and sufficient conditions for sparsity pattern recovery"

Connected through hyperedge HE-3 (induced by source paper "exact dynamic support tracking with multiple measurement vectors using compressive music"):
  with members:
    - "belief propagation for joint sparse recovery"
    - "support recovery of sparse signals in the presence of multiple measurement vectors"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "average case analysis of multichannel sparse recovery using convex relaxation"
    - "necessary and sufficient conditions for sparsity pattern recovery"

Connected through hyperedge HE-4 (induced by source paper "compressive music a missing link between compressive sensing and array signal processing"):
  with members:
    - "block sparse signals uncertainty relations and efficient recovery"
    - "theoretical and empirical results for recovery from multiple measurements"
    - "model based compressive sensing"
    - "rank awareness in joint sparse recovery"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "performance analysis for sparse support recovery"
    - "average case analysis of multichannel sparse recovery using convex relaxation"
    - "message passing algorithms for compressed 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)