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: "permutations unlabeled beyond sampling unknown"
  Abstract: A recent unlabeled sampling result by Unnikrishnan, Haghighatshoar, and Vetterli states that with probability one over Gaussian random matrices A with iid entries, any   $x$   can be uniquely recovered from an unknown permutation of   $y = A x$   as soon as   $A$   has at least twice as many rows as columns. We show that this condition on   $A$   implies something much stronger: that an unknown vector   $x$   can be recovered from measurements   $y = T A x$  , when the unknown   $T$   belongs to an arbitrary set of invertible, diagonalizable linear transformations   $\mathcal {T}$  . The set   $\mathcal {T}$   can be finite or countably infinite. When it is the set of   $m \times m$   permutation matrices, we have the classical unlabeled sampling problem. We show that for almost all   $A$   with at least twice as many rows as columns, all   $x$   can be recovered either uniquely, or up to a scale depending on   $\mathcal {T}$  , and that the condition on the size of   $A$   is necessary. Our proof is based on vector space geometry. Specializing to permutations, we obtain a simplified proof of the uniqueness result of Unnikrishnan, Haghighatshoar, and Vetterli. In this letter, we are only concerned with uniqueness; stability and algorithms are left for future work.

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"
    - "homomorphic sensing"
    - "signal recovery from unlabeled samples"
    - "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:
    - "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"
    - "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:
    - "homomorphic sensing"
    - "signal recovery from unlabeled samples"
    - "linear regression without correspondence"

Connected through hyperedge HE-4 (induced by source paper "an algebraic geometric approach for linear regression without correspondences"):
  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"
    - "dual principal component pursuit"
    - "filtrated algebraic subspace clustering"
    - "algebraic clustering of affine subspaces"

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)