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 "an out of sample extension of sparse subspace clustering and low rank representation for clustering large scale data sets"):
  Abstract: In this paper, we propose a general framework to address two problems in Sparse Subspace Clustering (SSC) and Low Rank Representation (LRR), i.e., scalability issue and out-of-sample problem. SSC and LRR are two recently proposed subspace clustering algorithms which construct a similarity graph for spectral clustering by using the sparsest and the lowest-rank coefficients, respectively. SSC and LRR have achieved state-of-the-art results in data clustering. However, their time complexities are very high so that it is inefficient to apply them to large scale data set. Moreover, SSC and LRR cannot cope with out-of-sample data that are not used to construct the similarity graph. For each new datum, they must recalculate the sparsest/lowest-rank coefficients and membership assignment matrix of the whole data set. This makes SSC and LRR not competitive to fast online learning algorithm. To overcome these problems, we propose a simple but effective method which makes SSC and LRR feasible to grouping new data and large scale data. The solution of our method adopts a "sampling, clustering, coding, and classifying" strategy. Specifically, we split the data into two parts, in-sample data and out-of-sample data, where out-of-sample data are located at the subspaces spanned by in-sample data; and obtain the cluster membership of in-sample data; after that, assign each out-of-sample datum to the nearest subspace that produces the minimal reconstruction error. Both theoretical analysis and experimental results show the efficacy of our methods.

Includes members:
  Member "learning locality constrained collaborative representation for robust face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "collaboratively weighting deep and classic representation via l2 regularization for image classification")
  Member "robust recovery of subspace structures by low rank representation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gated fusion network for degraded image super resolution")
  Member "fixed rank representation for unsupervised visual learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "k meansnet when k means meets differentiable programming")
  Member "sparse subspace clustering algorithm theory and applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "provably efficient exploration for rl with unsupervised learning")
  Member "robust principal component analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "exact matrix completion via convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")

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)