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: "adversarial top k ranking"
  Abstract: We study the top-$K$ ranking problem where the goal is to recover the set of top-$K$ ranked items out of a large collection of items based on partially revealed preferences. We consider an adversarial crowdsourced setting where there are two population sets, and pairwise comparison samples drawn from one of the populations follow the standard Bradley-Terry-Luce model (i.e., the chance of item $i$ beating item $j$ is proportional to the relative score of item $i$ to item $j$), while in the other population, the corresponding chance is inversely proportional to the relative score. When the relative size of the two populations is known, we characterize the minimax limit on the sample size required (up to a constant) for reliably identifying the top-$K$ items, and demonstrate how it scales with the relative size. Moreover, by leveraging a tensor decomposition method for disambiguating mixture distributions, we extend our result to the more realistic scenario in which the relative population size is unknown, thus establishing an upper bound on the fundamental limit of the sample size for recovering the top-$K$ set.

Connected through hyperedge HE-1 (induced by source paper "sorted top k in rounds"):
  with members:
    - "instance optimality in the noisy value and comparison model accept accept strong accept which papers get in"
    - "a nearly instance optimal algorithm for top k ranking under the multinomial logit model"
    - "competitive analysis of the top k ranking problem"
    - "parallel algorithms for select and partition with noisy comparisons"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "active ranking using pairwise comparisons"
    - "sorting from noisy information"

Connected through hyperedge HE-2 (induced by source paper "a ranking model motivated by nonnegative matrix factorization with applications to tennis tournaments"):
  with members:
    - "simple robust and optimal ranking from pairwise comparisons"
    - "a topic modeling approach to ranking"

Connected through hyperedge HE-3 (induced by source paper "from social to individuals a parsimonious path of multi level models for crowdsourced preference aggregation"):
  with members:
    - "robust subjective visual property prediction from crowdsourced pairwise labels"
    - "parsimonious mixed effects hodgerank for crowdsourced preference aggregation"
    - "cdas a crowdsourcing data analytics system"
    - "flows and decompositions of games harmonic and potential games"

Connected through hyperedge HE-4 (induced by source paper "a nearly instance optimal algorithm for top k ranking under the multinomial logit model"):
  with members:
    - "active ranking from pairwise comparisons and when parametric assumptions don t help"
    - "competitive analysis of the top k ranking problem"
    - "top k ranking from pairwise comparisons when spectral ranking is optimal"
    - "parallel algorithms for select and partition with noisy comparisons"
    - "spectral mle top k rank aggregation from pairwise comparisons"
    - "simple robust and optimal ranking from pairwise comparisons"
    - "active ranking using pairwise comparisons"
    - "noisy sorting without resampling"

Connected through hyperedge HE-5 (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"
    - "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"
    - "rank centrality ranking from pair wise comparisons"
    - "active ranking using pairwise comparisons"
    - "noisy sorting without resampling"

Connected through hyperedge HE-6 (induced by source paper "learning mixtures of plackett luce models"):
  with members:
    - "learning mixtures of ranking models"

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.IR(Information Retrieval)