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: "rank centrality ranking from pair wise comparisons"
  Abstract: The question of aggregating pair-wise comparisons to obtain a global ranking over a collection of objects has been of interest for a very long time: be it ranking of online gamers (e.g. MSR's TrueSkill system) and chess players, aggregating social opinions, or deciding which product to sell based on transactions. In most settings, in addition to obtaining a ranking, finding `scores' for each object (e.g. player's rating) is of interest for understanding the intensity of the preferences. #R##N#In this paper, we propose Rank Centrality, an iterative rank aggregation algorithm for discovering scores for objects (or items) from pair-wise comparisons. The algorithm has a natural random walk interpretation over the graph of objects with an edge present between a pair of objects if they are compared; the score, which we call Rank Centrality, of an object turns out to be its stationary probability under this random walk. To study the efficacy of the algorithm, we consider the popular Bradley-Terry-Luce (BTL) model (equivalent to the Multinomial Logit (MNL) for pair-wise comparisons) in which each object has an associated score which determines the probabilistic outcomes of pair-wise comparisons between objects. In terms of the pair-wise marginal probabilities, which is the main subject of this paper, the MNL model and the BTL model are identical. We bound the finite sample error rates between the scores assumed by the BTL model and those estimated by our algorithm. In particular, the number of samples required to learn the score well with high probability depends on the structure of the comparison graph. When the Laplacian of the comparison graph has a strictly positive spectral gap, e.g. each item is compared to a subset of randomly chosen items, this leads to dependence on the number of samples that is nearly order-optimal.

Connected through hyperedge HE-1 (induced by source paper "adaptive sampling for coarse ranking"):
  with members:
    - "maximum selection and ranking under noisy comparisons"
    - "practical algorithms for best k identification in multi armed bandits"
    - "active ranking from pairwise comparisons and when parametric assumptions don t help"
    - "deep learning the city quantifying urban perception at a global scale"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "active ranking using pairwise comparisons"
    - "an active learning algorithm for ranking from pairwise preferences with an almost optimal query complexity"
    - "sorting from noisy information"

Connected through hyperedge HE-2 (induced by source paper "data driven rank breaking for efficient rank aggregation"):
  with members:
    - "collaboratively learning preferences from ordinal data"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "estimation from pairwise comparisons sharp minimax bounds with topology dependence"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "learning mixed membership mallows models from pairwise comparisons"
    - "efficient estimation of word representations in vector space"
    - "random utility theory for social choice"
    - "active ranking using pairwise comparisons"

Connected through hyperedge HE-3 (induced by source paper "competitive analysis of the top k ranking problem"):
  with members:
    - "top k ranking from pairwise comparisons when spectral ranking is optimal"
    - "adversarial top k ranking"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "simulating noisy channel interaction"
    - "multiple identifications in multi armed bandits"
    - "active ranking using pairwise comparisons"
    - "noisy sorting without resampling"

Connected through hyperedge HE-4 (induced by source paper "computational and statistical tradeoffs in learning to rank"):
  with members:
    - "data driven rank breaking for efficient rank aggregation"
    - "improved sum of squares lower bounds for hidden clique and hidden submatrix problems"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "estimation from pairwise comparisons sharp minimax bounds with topology dependence"
    - "sum of squares lower bounds for planted clique"
    - "random utility theory for social choice"

Connected through hyperedge HE-5 (induced by source paper "adversarial top k ranking"):
  with members:
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "online tensor methods for learning latent variable models"
    - "tensor decompositions for learning latent variable models"
    - "results on the redundancy of universal compression for finite length sequences"

Connected through hyperedge HE-6 (induced by source paper "rank aggregation for course sequence discovery"):
  with members:
    - "mapping the curricular structure and contents of network science courses"
    - "personalized course sequence recommendations"
    - "sync rank robust ranking constrained ranking and rank aggregation via eigenvector and semidefinite programming synchronization"
    - "methods for ordinal peer grading"
    - "pagerank beyond the web"
    - "least squares ranking on graphs"

Connected through hyperedge HE-7 (induced by source paper "randomized kaczmarz for rank aggregation from pairwise comparisons"):
  with members:
    - "collaboratively learning preferences from ordinal data"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "estimation from pairwise comparisons sharp minimax bounds with topology dependence"
    - "active ranking using pairwise comparisons"
    - "least squares ranking on graphs"
    - "sorting from noisy information"

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)