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: "signal recovery from unlabeled samples"
  Abstract: In this paper, we study the recovery of a signal from a set of noisy linear projections (measurements), when such projections are unlabeled, that is, the correspondence between the measurements and the set of projection vectors (i.e., the rows of the measurement matrix) is not known  a priori . We consider a special case of unlabeled sensing referred to as  unlabeled ordered sampling  (UOS) where the ordering of the measurements is preserved. We identify a natural duality between this problem and classical  compressed sensing   (CS), where we show that the unknown support (location of nonzero elements) of a sparse signal in CS corresponds to the unknown indices of the measurements in UOS. While in CS, it is possible to recover a sparse signal from an underdetermined set of linear equations (less equations than the signal dimension), successful recovery in UOS requires taking more samples than the dimension of the signal. Motivated by this duality, we develop a   restricted isometry property  (RIP) similar to that in CS. We also design a low-complexity alternating minimization algorithm that achieves a stable signal recovery under the established RIP. We analyze our proposed algorithm for different signal dimensions and number of measurements theoretically and investigate its performance empirically via numerical simulations. The results are reminiscent of a phase transition occurring in CS.

Connected through hyperedge HE-1 (induced by source paper "linear regression without correspondences via concave minimization"):
  with members:
    - "permutation recovery from multiple measurement vectors in unlabeled sensing"
    - "permutations unlabeled beyond sampling unknown"
    - "homomorphic sensing"
    - "an algebraic geometric approach for linear regression without correspondences"
    - "linear regression without correspondence"
    - "unlabeled sensing with random linear measurements"

Connected through hyperedge HE-2 (induced by source paper "homomorphic sensing"):
  with members:
    - "permutations unlabeled beyond sampling unknown"
    - "an algebraic geometric approach to shuffled linear regression"
    - "linear regression without correspondence"
    - "go icp a globally optimal solution to 3d icp point set registration"
    - "unlabeled sensing with random linear measurements"
    - "point set registration coherent point drift"

Connected through hyperedge HE-3 (induced by source paper "permutation recovery from multiple measurement vectors in unlabeled sensing"):
  with members:
    - "permutations unlabeled beyond sampling unknown"
    - "homomorphic sensing"
    - "linear regression without correspondence"

Connected through hyperedge HE-4 (induced by source paper "permutations unlabeled beyond sampling unknown"):
  with members:
    - "reconstructing point sets from distance distributions"
    - "an algebraic geometric approach to shuffled linear regression"
    - "unlabeled sensing with random linear measurements"

Connected through hyperedge HE-5 (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"
    - "linear regression without correspondence"
    - "unlabeled sensing with random linear measurements"
    - "dual principal component pursuit"
    - "filtrated algebraic subspace clustering"
    - "algebraic clustering of affine subspaces"

Connected through hyperedge HE-6 (induced by source paper "an algebraic geometric approach to shuffled linear regression"):
  with members:
    - "homomorphic sensing"
    - "an overview of robust subspace recovery"
    - "linear regression without correspondence"
    - "unlabeled sensing with random linear measurements"
    - "filtrated algebraic subspace clustering"
    - "timing channels with multiple identical quanta"
    - "blind compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "unlabelled sensing a sparse bayesian learning approach"):
  with members:
    - "signal amplitude estimation and detection from unlabeled binary quantized samples"
    - "unlabeled sensing with random linear measurements"
    - "on the computational intractability of exact and approximate dictionary learning"

Connected through hyperedge HE-8 (induced by source paper "signal amplitude estimation and detection from unlabeled binary quantized samples"):
  with members:
    - "unlabeled sensing with random linear measurements"
    - "universal and composite hypothesis testing via mismatched divergence"

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)