human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "reconstructing point sets from distance distributions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "solving complex quadratic systems with full rank random matrices")
  Member "signal recovery from unlabeled samples":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "linear regression without correspondences via concave minimization")
  Member "an algebraic geometric approach to shuffled linear regression":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "homomorphic sensing")
  Member "unlabeled sensing with random linear measurements":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "linear regression without correspondences via concave minimization")

Question: Please tell me which class the source paper 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)