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: "collaborative spectrum sensing from sparse observations using matrix completion for cognitive radio networks"
  Abstract: In cognitive radio, spectrum sensing is a key component to detect spectrum holes (i.e., channels not used by any primary users). Collaborative spectrum sensing among the cognitive radio nodes is expected to improve the ability of checking complete spectrum usage states. Unfortunately, due to power limitation and channel fading, available channel sensing information is far from being sufficient to tell the unoccupied channels directly. Aiming at breaking this bottleneck, we apply recent matrix completion techniques to greatly reduce the sensing information needed. We formulate the collaborative sensing problem as a matrix completion subproblem and a joint-sparsity reconstruction subproblem. Results of numerical simulations that validated the effectiveness and robustness of the proposed approach are presented. In particular, in noiseless cases, when number of primary user is small, exact detection was obtained with no more than 8% of the complete sensing information, whilst as number of primary user increases, to achieve a detection rate of 95.55%, the required information percentage was merely 16.8%.

Connected through hyperedge HE-1 (induced by source paper "fast singular value shrinkage with chebyshev polynomial approximation based on signal sparsity"):
  with members:
    - "fast randomized singular value thresholding for nuclear norm minimization"
    - "an algorithm for improving non local means operators via low rank approximation"
    - "low rank modeling and its applications in image analysis"
    - "convexity in source separation models geometry and algorithms"
    - "moving object detection by detecting contiguous outliers in the low rank representation"
    - "the power of convex relaxation near optimal matrix completion"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-2 (induced by source paper "analysis and design of irregular graphs for node based verification based recovery algorithms in compressed sensing"):
  with members:
    - "compressive identification of linear operators"
    - "on the iterative decoding of high rate ldpc codes with applications in compressed sensing"
    - "analysis of verification based decoding on the q ary symmetric channel for large q"
    - "combining geometry and combinatorics a unified approach to sparse signal recovery"

Connected through hyperedge HE-3 (induced by source paper "strongly convex programming for exact matrix completion and robust principal component analysis"):
  with members:
    - "recovering low rank matrices from few coefficients in any basis"
    - "robust principal component analysis exact recovery of corrupted low rank matrices"
    - "the power of convex relaxation near optimal matrix completion"
    - "a simpler approach to matrix completion"
    - "matrix completion with noise"
    - "robust principal component analysis"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-4 (induced by source paper "collaborative spectrum sensing from sparse observations in cognitive radio networks"):
  with members:
    - "dynamic updating for ell_ 1 minimization"
    - "matrix completion from a few entries"
    - "coalitional games for distributed collaborative spectrum sensing in cognitive radio networks"
    - "cooperative spectrum sensing using random matrix theory"

Connected through hyperedge HE-5 (induced by source paper "compressive sensing for dynamic spectrum access networks techniques and tradeoffs"):
  with members:
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "a block lanczos with warm start technique for accelerating nuclear norm minimization algorithms"):
  with members:
    - "stable principal component pursuit"
    - "dense error correction for low rank matrices via principal component pursuit"
    - "matrix completion with noise"
    - "robust principal component analysis"

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)