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: "homomorphic sensing"
  Abstract: A recent line of research termed unlabeled sensing and shuffled linear regression has been exploring under great generality the recovery of signals from subsampled and permuted measurements; a challenging problem in diverse fields of data science and machine learning. In this paper we introduce an abstraction of this problem which we call homomorphic sensing. Given a linear subspace and a finite set of linear transformations we develop an algebraic theory which establishes conditions guaranteeing that points in the subspace are uniquely determined from their homomorphic image under some transformation in the set. As a special case, we recover known conditions for unlabeled sensing, as well as new results and extensions. On the algorithmic level we exhibit two dynamic programming based algorithms, which to the best of our knowledge are the first working solutions for the unlabeled sensing problem for small dimensions. One of them, additionally based on branch-and-bound, when applied to image registration under affine transformations, performs on par with or outperforms state-of-the-art methods on benchmark datasets.

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"
    - "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 "permutation recovery from multiple measurement vectors in unlabeled sensing"):
  with members:
    - "permutations unlabeled beyond sampling unknown"
    - "signal recovery from unlabeled samples"
    - "linear regression without correspondence"

Connected through hyperedge HE-3 (induced by source paper "an algebraic geometric approach for linear regression without correspondences"):
  with members:
    - "permutations unlabeled beyond sampling unknown"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "an algebraic geometric approach to shuffled linear regression"):
  with members:
    - "an overview of robust subspace recovery"
    - "signal recovery from unlabeled samples"
    - "linear regression without correspondence"
    - "unlabeled sensing with random linear measurements"
    - "filtrated algebraic subspace clustering"
    - "timing channels with multiple identical quanta"
    - "blind compressed sensing"

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)