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: "clustering using max norm constrained optimization"
  Abstract: We suggest using the max-norm as a convex surrogate constraint for clustering. We show how this yields a better exact cluster recovery guarantee than previously suggested nuclear-norm relaxation, and study the effectiveness of our method, and other related convex relaxations, compared to other clustering approaches.

Connected through hyperedge HE-1 (induced by source paper "approximating max cut under graph mso constraints"):
  with members:
    - "simplified algorithmic metatheorems beyond mso treewidth and neighborhood diversity"
    - "approximation algorithms for connected maximum cut and related problems"
    - "submodular function maximization via the multilinear relaxation and contention resolution schemes"

Connected through hyperedge HE-2 (induced by source paper "near optimal joint object matching via convex relaxation"):
  with members:
    - "multireference alignment using semidefinite programming"
    - "global registration of multiple point clouds using semidefinite programming"
    - "exact and stable recovery of rotations for robust synchronization"
    - "clustering partially observed graphs via convex optimization"
    - "low rank matrix recovery from errors and erasures"
    - "matrix completion from noisy entries"
    - "robust principal component analysis"
    - "learning graph matching"

Connected through hyperedge HE-3 (induced by source paper "clustering hamming embedding generalized lsh and the max norm"):
  with members:
    - "the power of asymmetry in binary hashing"
    - "clustering partially observed graphs via convex optimization"

Connected through hyperedge HE-4 (induced by source paper "learning the dependence graph of time series with latent factors"):
  with members:
    - "clustering partially observed graphs via convex optimization"
    - "low rank matrix recovery from errors and erasures"
    - "stable principal component pursuit"
    - "matrix completion with noise"
    - "robust principal component analysis"

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)