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: "unlabeled sensing with random linear measurements"
  Abstract: We study the problem of solving a linear sensing system when the observations are unlabeled. Specifically we seek a solution to a linear system of equations y = Ax when the order of the observations in the vector y is unknown. Focusing on the setting in which A is a random matrix with i.i.d. entries, we show that if the sensing matrix A admits an oversampling ratio of 2 or higher, then with probability 1 it is possible to recover x exactly without the knowledge of the order of the observations in y. Furthermore, if x is of dimension K, then any 2K entries of y are sufficient to recover x. This result implies the existence of deterministic unlabeled sensing matrices with an oversampling factor of 2 that admit perfect reconstruction. The result is universal in that recovery is guaranteed for all possible choices of x. While the proof is constructive, it uses a combinatorial algorithm which is not practical, leaving the question of complexity open. We also analyze a noisy version of the problem and show that local stability is guaranteed by the solution. In particular, for every x, the recovery error tends to zero as the signal-to-noise-ratio tends to infinity. The question of universal stability is unclear. We also obtain a converse of the result in the noiseless case: If the number of observations in y is less than 2K, then with probability 1, universal recovery fails, i.e., with probability 1, there exists distinct choices of x which lead to the same unordered list of observations in y. In terms of applications, the unlabeled sensing problem is related to data association problems encountered in different domains including robotics where it is appears in a method called "simultaneous localization and mapping" (SLAM), multi-target tracking applications, and in sampling signals in the presence of jitter.

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"
    - "signal recovery from unlabeled samples"
    - "an algebraic geometric approach for linear regression without correspondences"
    - "linear regression without correspondence"

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

Connected through hyperedge HE-3 (induced by source paper "permutations unlabeled beyond sampling unknown"):
  with members:
    - "reconstructing point sets from distance distributions"
    - "signal recovery from unlabeled samples"
    - "an algebraic geometric approach to shuffled linear regression"

Connected through hyperedge HE-4 (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"
    - "dual principal component pursuit"
    - "filtrated algebraic subspace clustering"
    - "algebraic clustering of affine subspaces"

Connected through hyperedge HE-5 (induced by source paper "signal recovery from unlabeled samples"):
  with members:
    - "linear regression without correspondence"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "through the haze a non convex approach to blind gain calibration for linear random sensing models"
    - "self calibration and biconvex compressive sensing"
    - "timing channels with multiple identical quanta"
    - "general deviants an analysis of perturbations in compressed sensing"
    - "iterative hard thresholding for compressed sensing"

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"
    - "signal recovery from unlabeled samples"
    - "linear regression without correspondence"
    - "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 recovery from unlabeled samples"
    - "signal amplitude estimation and detection from unlabeled binary quantized samples"
    - "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:
    - "signal recovery from unlabeled samples"
    - "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)