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: "correlation clustering with low rank matrices"
  Abstract: Correlation clustering is a technique for aggregating data based on qualitative information about which pairs of objects are labeled 'similar' or 'dissimilar.' Because the optimization problem is NP-hard, much of the previous literature focuses on finding approximation algorithms. In this paper we explore a new approach to correlation clustering by considering how to solve the problem when the data to be clustered can be represented by a low-rank matrix. Many real-world datasets are known to be inherently low-dimensional, and our goal is to establish a tractable approach to correlation clustering in this important setting. We prove in particular that correlation clustering can be solved in polynomial time when the underlying matrix is positive semidefinite with small constant rank, but that the task remains NP-hard in the presence of even one negative eigenvalue. Based on our theoretical results, we develop an algorithm for efficiently solving low-rank positive semidefinite correlation clustering by employing a procedure for zonotope vertex enumeration. We demonstrate the effectiveness and speed of our algorithm by using it to solve several clustering problems on both synthetic and real-world data.

Connected through hyperedge HE-1 (induced by source paper "hierarchical correlation clustering and tree preserving embedding"):
  with members:
    - "correlation clustering with adaptive similarity queries"
    - "hierarchical clustering via spreading metrics"
    - "approximate hierarchical clustering via sparsest cut and spreading metrics"
    - "a cost function for similarity based hierarchical clustering"
    - "robust hierarchical clustering"

Connected through hyperedge HE-2 (induced by source paper "simple and practical algorithms for ell_p norm low rank approximation"):
  with members:
    - "on the complexity of robust pca and l1 norm low rank matrix approximation"
    - "a general and adaptive robust loss function"
    - "low rank matrix approximation in the infinity norm"
    - "algorithms for ell_p low rank approximation"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "recovery guarantee of non negative matrix factorization via alternating updates"
    - "matrix completion has no spurious local minimum"
    - "symmetry saddle points and global geometry of nonconvex matrix factorization"

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.LG(Machine Learning)