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: "communications inspired projection design with application to compressive sensing"
  Abstract: We consider the recovery of an underlying signal $\mathbf{x}\in\mathbb{C}^m$ based on projection measurements of the form $\mathbf{y}=\mathbf{M}\mathbf{x}+\mathbf{w}$, where $\mathbf{y}\in\mathbb{C}^\ell$ and $\mathbf{w}$ is measurement noise; we are interested in the case $\ell\ll m$. It is assumed that the signal model $p(\mathbf{x})$ is known and that $\mathbf{w}\sim\mathcal{CN}(\mathbf{w};\boldsymbol{0},\bf \Sigma_w)$ for known $\bf \Sigma_w$. The objective is to design a projection matrix $\mathbf{M}\in\mathbb{C}^{\ell\times m}$ to maximize key information-theoretic quantities with operational significance, including the mutual information between the signal and the projections $\mathcal{I}(\mathbf{x};\mathbf{y})$ or the Renyi entropy of the projections $\mbox{h}_\alpha \left( \mathbf{y} \right)$ (Shannon entropy is a special case). By capitalizing on explicit characterizations of the gradients of the information measures with respect to the projection matrix, where we also partially extend the well-...

Connected through hyperedge HE-1 (induced by source paper "measurement matrix design for phase retrieval based on mutual information"):
  with members:
    - "non convex phase retrieval from stft measurements"
    - "stft phase retrieval uniqueness guarantees and recovery algorithms"
    - "intensity only optical compressive imaging using a multiply scattering material and a double phase retrieval approach"
    - "almost lossless analog compression without phase information"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "phasecode fast and efficient compressive phase retrieval based on sparse graph codes"
    - "phase retrieval from coded diffraction patterns"
    - "phase retrieval stability and recovery guarantees"

Connected through hyperedge HE-2 (induced by source paper "optimizing matrices for compressed sensing using existing goodness measures negative results and an alternative"):
  with members:
    - "optimizing codes for source separation in compressed video recovery and color image demosaicing"
    - "coded aperture compressive temporal imaging"
    - "stable restoration and separation of approximately sparse signals"
    - "new bounds for restricted isometry constants"

Connected through hyperedge HE-3 (induced by source paper "reduced electron exposure for energy dispersive spectroscopy using dynamic sampling"):
  with members:
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "classification and reconstruction of high dimensional signals from low dimensional features in the presence of side information"):
  with members:
    - "reconstruction of signals drawn from a gaussian mixture via noisy compressive measurements"
    - "multi terminal probabilistic compressed sensing"
    - "compressed sensing with prior information optimal strategies geometry and bounds"
    - "compressive classification of a mixture of gaussians analysis designs and geometrical interpretation"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "task driven adaptive statistical compressive sensing of gaussian mixture models"
    - "exact recovery conditions for sparse representations with partial support information"
    - "approximate message passing based compressed sensing reconstruction with generalized elastic net prior"

Connected through hyperedge HE-5 (induced by source paper "string submodular functions with curvature constraints"):
  with members:
    - "a tight combinatorial algorithm for submodular maximization subject to a matroid constraint"
    - "submodularity and optimality of fusion rules in balanced binary relay trees"
    - "maximizing sequence submodular functions and its application to online advertising"
    - "adaptive submodularity theory and applications in active learning and stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "optimizing codes for source separation in color image demosaicing and compressive video recovery"):
  with members:
    - "stable restoration and separation of approximately sparse signals"
    - "new bounds for restricted isometry constants"

Connected through hyperedge HE-7 (induced by source paper "optimizing codes for source separation in compressed video recovery and color image demosaicing"):
  with members:
    - "stable restoration and separation of approximately sparse signals"
    - "new bounds for restricted isometry constants"

Connected through hyperedge HE-8 (induced by source paper "sequential information guided sensing"):
  with members:
    - "info greedy sequential adaptive compressed sensing"
    - "exact and stable covariance estimation from quadratic sampling via convex programming"
    - "near optimal adaptive compressed sensing"
    - "task driven adaptive statistical compressive sensing of gaussian mixture models"
    - "on the fundamental limits of recovering tree sparse vectors from noisy linear measurements"
    - "sketching sparse matrices"
    - "compressive binary search"
    - "sequential 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)