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: "a topic modeling approach to ranking"
  Abstract: We propose a topic modeling approach to the prediction of preferences in pairwise comparisons. We develop a new generative model for pairwise comparisons that accounts for multiple shared latent rankings that are prevalent in a population of users. This new model also captures inconsistent user behavior in a natural way. We show how the estimation of latent rankings in the new generative model can be formally reduced to the estimation of topics in a statistically equivalent topic modeling problem. We leverage recent advances in the topic modeling literature to develop an algorithm that can learn shared latent rankings with provable consistency as well as sample and computational complexity guarantees. We demonstrate that the new approach is empirically competitive with the current state-of-the-art approaches in predicting preferences on some semi-synthetic and real world datasets.

Connected through hyperedge HE-1 (induced by source paper "a ranking model motivated by nonnegative matrix factorization with applications to tennis tournaments"):
  with members:
    - "adversarial top k ranking"
    - "simple robust and optimal ranking from pairwise comparisons"

Connected through hyperedge HE-2 (induced by source paper "inductive pairwise ranking going beyond the n log n barrier"):
  with members:
    - "provable inductive matrix completion"
    - "robust principal component analysis"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-3 (induced by source paper "simple robust and optimal ranking from pairwise comparisons"):
  with members:
    - "feeling the bern adaptive estimators for bernoulli probabilities of pairwise comparisons"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "estimation from pairwise comparisons sharp minimax bounds with topology dependence"
    - "active ranking using pairwise comparisons"
    - "noisy sorting without resampling"
    - "optimal aggregation algorithms for middleware"

Connected through hyperedge HE-4 (induced by source paper "necessary and sufficient conditions and a provably efficient algorithm for separable topic discovery"):
  with members:
    - "on some provably correct cases of variational inference for topic models"
    - "a tensor approach to learning mixed membership community models"
    - "a practical algorithm for topic modeling with provable guarantees"
    - "on the complexity of nonnegative matrix factorization"

Connected through hyperedge HE-5 (induced by source paper "learning mixed membership mallows models from pairwise comparisons"):
  with members:
    - "learning mixtures of ranking models"
    - "scalable methods for nonnegative matrix factorizations of near separable tall and skinny matrices"
    - "a practical algorithm for topic modeling with provable guarantees"

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)