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 "clustering is semidefinitely not that hard nonnegative sdp for manifold disentangling"):
  Abstract: In solving hard computational problems, semidefinite program (SDP) relaxations often play an important role because they come with a guarantee of optimality. Here, we focus on a popular semidefinite relaxation of K-means clustering which yields the same solution as the non-convex original formulation for well segregated datasets. We report an unexpected finding: when data contains (greater than zero-dimensional) manifolds, the SDP solution captures such geometrical structures. Unlike traditional manifold embedding techniques, our approach does not rely on manually defining a kernel but rather enforces locality via a nonnegativity constraint. We thus call our approach NOnnegative MAnifold Disentangling, or NOMAD. To build an intuitive understanding of its manifold learning capabilities, we develop a theoretical analysis of NOMAD on idealized datasets. While NOMAD is convex and the globally optimal solution can be found by generic SDP solvers with polynomial time complexity, they are too slow for modern datasets. To address this problem, we analyze a non-convex heuristic and present a new, convex and yet efficient, algorithm, based on the conditional gradient method. Our results render NOMAD a versatile, understandable, and powerful tool for manifold learning.

Includes members:
  Member "compressed nonnegative matrix factorization is fast and accurate":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "compressed factorization fast and accurate low rank factorization of compressively sensed data")
  Member "on the tightness of an sdp relaxation of k means":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "probably certifiably correct k means clustering")
  Member "on semidefinite relaxations for the block model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "statistical guarantees for local graph clustering")
  Member "a biclustering framework for consensus problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "nonnegative matrix underapproximation for robust multiple model fitting")
  Member "an alternating direction algorithm for matrix completion with nonnegative factors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a group norm regularized lrr factorization model for spectral clustering")
  Member "algorithms for learning kernels based on centered alignment":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "entangled watermarks as a defense against model extraction")
  Member "a new embedding quality assessment method for manifold learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a review framework and r toolkit for exploring evaluating and comparing visualizations")

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)