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 collaborative compressive sensing systems the framework design and algorithm"
  Abstract: We propose a collaborative compressive sensing (CCS) framework consisting of a bank of $K$ compressive sensing (CS) systems that share the same sensing matrix but have different sparsifying dictionaries. This CCS system is guaranteed to yield better performance than each individual CS system in a statistical sense, while with the parallel computing strategy, it requires the same time as that needed for each individual CS system to conduct compression and signal recovery. We then provide an approach to designing optimal CCS systems by utilizing a measure that involves both the sensing matrix and dictionaries and hence allows us to simultaneously optimize the sensing matrix and all the $K$ dictionaries under the same scheme. An alternating minimization-based algorithm is derived for solving the corresponding optimal design problem. We provide a rigorous convergence analysis to show that the proposed algorithm is convergent. Experiments with real images are carried out and show that the proposed CCS system significantly improves on existing CS systems in terms of the signal recovery accuracy.

Connected through hyperedge HE-1 (induced by source paper "compressed sensing with probability based prior information"):
  with members:
    - "subspace orbit randomized decomposition for low rank matrix approximations"
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "masked signal decomposition using subspace representation and its applications"
    - "widely linear precoding for large scale mimo with iqi algorithms and performance analysis"
    - "joint sensing matrix and sparsifying dictionary optimization for tensor compressive sensing"
    - "iterative detection and decoding algorithms for mimo systems in block fading channels using ldpc codes"
    - "robust adaptive beamforming based on low rank and cross correlation techniques"
    - "a survey of sparse representation algorithms and applications"

Connected through hyperedge HE-2 (induced by source paper "online learning sensing matrix and sparsifying dictionary simultaneously for compressive sensing"):
  with members:
    - "global optimality in low rank matrix optimization"
    - "trainlets dictionary learning in high dimensions"
    - "seboost boosting stochastic learning using subspace optimization techniques"
    - "an efficient method for robust projection matrix design"
    - "approximating sampled sinusoids and multiband signals using multiband modulated dpss dictionaries"
    - "projection design for statistical compressive sensing a tight frame based approach"

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)