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 "linear regression without correspondences via concave minimization"):
  Abstract: Linear regression without correspondences concerns the recovery of a signal in the linear regression setting, where the correspondences between the observations and the linear functionals are unknown. The associated maximum likelihood function is NP-hard to compute when the signal has dimension larger than one. To optimize this objective function we reformulate it as a concave minimization problem, which we solve via branch-and-bound. This is supported by a computable search space to branch, an effective lower bounding scheme via convex envelope minimization and a refined upper bound, all naturally arising from the concave minimization reformulation. The resulting algorithm outperforms state-of-the-art methods for fully shuffled data and remains tractable for up to $8$-dimensional signals, an untouched regime in prior work.

Includes members:
  Member "permutation recovery from multiple measurement vectors in unlabeled sensing":
    also connected through other hyperedges:
      (none)
  Member "permutations unlabeled beyond sampling unknown":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "homomorphic sensing")
  Member "homomorphic sensing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "permutation recovery from multiple measurement vectors in unlabeled sensing")
  Member "signal recovery from unlabeled samples":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "homomorphic sensing")
  Member "an algebraic geometric approach for linear regression without correspondences":
    also connected through other hyperedges:
      (none)
  Member "linear regression without correspondence":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "homomorphic sensing")
  Member "unlabeled sensing with random linear measurements":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "homomorphic sensing")

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)