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: "blind compressed sensing"
  Abstract: The fundamental principle underlying compressed sensing is that a signal, which is sparse under some basis representation, can be recovered from a small number of linear measurements. However, prior knowledge of the sparsity basis is essential for the recovery process. This work introduces the concept of blind compressed sensing, which avoids the need to know the sparsity basis in both the sampling and the recovery process. We suggest three possible constraints on the sparsity basis that can be added to the problem in order to make its solution unique. For each constraint we prove conditions for uniqueness, and suggest a simple method to retrieve the solution. Under the uniqueness conditions, and as long as the signals are sparse enough, we demonstrate through simulations that without knowing the sparsity basis our methods can achieve results similar to those of standard compressed sensing, which relay on prior knowledge of the sparsity basis. This offers a general sampling and reconstruction system that fits all sparse signals, regardless of the sparsity basis, under the conditions and constraints presented in this work.

Connected through hyperedge HE-1 (induced by source paper "an algebraic geometric approach for linear regression without correspondences"):
  with members:
    - "permutations unlabeled beyond sampling unknown"
    - "homomorphic sensing"
    - "an overview of robust subspace recovery"
    - "signal recovery from unlabeled samples"
    - "linear regression without correspondence"
    - "unlabeled sensing with random linear measurements"
    - "dual principal component pursuit"
    - "filtrated algebraic subspace clustering"

Connected through hyperedge HE-2 (induced by source paper "an algebraic geometric approach to shuffled linear regression"):
  with members:
    - "homomorphic sensing"
    - "an overview of robust subspace recovery"
    - "signal recovery from unlabeled samples"
    - "linear regression without correspondence"
    - "unlabeled sensing with random linear measurements"
    - "filtrated algebraic subspace clustering"
    - "timing channels with multiple identical quanta"

Connected through hyperedge HE-3 (induced by source paper "blind demixing and deconvolution at near optimal rate"):
  with members:
    - "regularized gradient descent a nonconvex recipe for fast joint blind deconvolution and demixing"
    - "recovering structured data from superimposed non linear measurements"
    - "robust nonnegative sparse recovery and the nullspace property of 0 1 measurements"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "low rank tensor recovery via iterative hard thresholding"
    - "optimal injectivity conditions for bilinear inverse problems with applications to identifiability of deconvolution problems"
    - "self calibration and biconvex compressive sensing"
    - "rip like properties in subsampled blind deconvolution"

Connected through hyperedge HE-4 (induced by source paper "deeprotect enabling inference based access control on mobile sensing applications"):
  with members:
    - "6thsense a context aware sensor based attack detector for smart devices"
    - "multi sensor authentication to improve smartphone security"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "caffe convolutional architecture for fast feature embedding"
    - "touchalytics on the applicability of touchscreen input as a behavioral biometric for continuous authentication"
    - "participatory privacy enabling privacy in participatory sensing"
    - "placeraider virtual theft in physical spaces with smartphones"

Connected through hyperedge HE-5 (induced by source paper "deterministic coding theorems for blind sensing optimal measurement rate and fractal dimension"):
  with members:
    - "efficient blind compressed sensing using sparsifying transforms with convergence guarantees and application to mri"
    - "on landau s eigenvalue theorem and information cut sets"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "compressive sensing of analog signals using discrete prolate spheroidal sequences"
    - "optimal phase transitions in compressed sensing"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"

Connected through hyperedge HE-6 (induced by source paper "active sensing of social networks"):
  with members:
    - "signal processing on graphs modeling causal relations in big data"
    - "the social system identification problem"
    - "analysis and control of beliefs in social networks"
    - "graph theoretic distributed inference in social networks"
    - "ergodic randomized algorithms and dynamics over networks"
    - "opinion fluctuations and disagreement in social networks"
    - "social structure of facebook networks"
    - "a unique nonnegative solution to an underdetermined system from vectors to matrices"

Connected through hyperedge HE-7 (induced by source paper "deep blind compressed sensing"):
  with members:
    - "unsupervised deep feature extraction for remote sensing image classification"

Connected through hyperedge HE-8 (induced by source paper "sparse approximation based on a random overcomplete basis"):
  with members:
    - "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)