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 "theoretical analysis of sparse subspace clustering with missing entries"):
  Abstract: Sparse Subspace Clustering (SSC) is a popular unsupervised machine learning method for clustering data lying close to a union of low-dimensional linear subspaces; a problem with numerous applications in pattern recognition and computer vision. Even though the behavior of SSC for uncorrupted data is by now well-understood, little is known about its theoretical properties when applied to data with missing entries. In this paper we give the first interpretable theoretical guarantees for SSC with incomplete data, and analytically establish that projecting the zero-filled data onto the observation patten of the point being expressed leads to a substantial improvement in performance. Since the projection induces further missing entries, this is a remarkable phenomenon, whose significance potentially extends to the entire class of self-expressive methods.

Includes members:
  Member "hyperplane clustering via dual principal component pursuit":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "oracle based active set algorithm for scalable elastic net subspace clustering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi level representation learning for deep subspace clustering")
  Member "on deterministic conditions for subspace clustering under missing data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a note on information theoretic bounds on matrix completion under union of subspaces model")
  Member "filtrated algebraic subspace clustering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an algebraic geometric approach for linear regression without correspondences")
  Member "robust subspace clustering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "provably efficient exploration for rl with unsupervised learning")
  Member "robust recovery of subspace structures by low rank representation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gated fusion network for degraded image super resolution")
  Member "generalized principal component analysis gpca":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "salient object detection a survey")
  Member "sparse subspace clustering algorithm theory and applications":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "provably efficient exploration for rl with unsupervised learning")

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.LG(Machine Learning)