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: "sorting and selection in posets"
  Abstract: Classical problems of sorting and searching assume an underlying linear ordering of the objects being compared. In this paper, we study a more general setting, in which some pairs of objects are incomparable. This generalization is relevant in applications related to rankings in sports, college admissions, or conference submissions. It also has potential applications in biology, such as comparing the evolutionary fitness of different strains of bacteria, or understanding input-output relations among a set of metabolic reactions or the causal influences among a set of interacting genes or proteins. Our results improve and extend results from two decades ago of Faigle and Tur\'{a}n. #R##N#A measure of complexity of a partially ordered set (poset) is its width. Our algorithms obtain information about a poset by queries that compare two elements. We present an algorithm that sorts, i.e. completely identifies, a width w poset of size n and has query complexity O(wn + nlog(n)), which is within a constant factor of the information-theoretic lower bound. We also show that a variant of Mergesort has query complexity O(wn(log(n/w))) and total complexity O((w^2)nlog(n/w)). Faigle and Tur\'{a}n have shown that the sorting problem has query complexity O(wn(log(n/w))) but did not address its total complexity. #R##N#For the related problem of determining the minimal elements of a poset, we give efficient deterministic and randomized algorithms with O(wn) query and total complexity, along with matching lower bounds for the query complexity up to a factor of 2. We generalize these results to the k-selection problem of determining the elements of height at most k. We also derive upper bounds on the total complexity of some other problems of a similar flavor.

Connected through hyperedge HE-1 (induced by source paper "decoy bandits dueling on a poset"):
  with members:
    - "copeland dueling bandits"
    - "reducing dueling bandits to cardinal bandits"
    - "relative upper confidence bound for the k armed dueling bandit problem"

Connected through hyperedge HE-2 (induced by source paper "sorting under 1 infty cost model"):
  with members:
    - "sorting and selection with random costs"

Connected through hyperedge HE-3 (induced by source paper "representing and reasoning with qualitative preferences for compositional systems"):
  with members:
    - "graphical models for preference and utility"
    - "ucp networks a directed graphical representation of conditional utilities"
    - "generic preferences over subsets of structured objects"
    - "the computational complexity of dominance and consistency in cp nets"
    - "on graphical modeling of preference and importance"
    - "cp nets a tool for representing and reasoning with conditional ceteris paribus preference statements"

Connected through hyperedge HE-4 (induced by source paper "lrm trees compressed indices adaptive sorting and compressed permutations"):
  with members:
    - "compressed representations of permutations and applications"
    - "optimal succinctness for range minimum queries"

Connected through hyperedge HE-5 (induced by source paper "searching in dynamic tree like partial orders"):
  with members:
    - "on the complexity of searching in trees average case minimization"

Connected through hyperedge HE-6 (induced by source paper "sorting under partial information without the ellipsoid algorithm"):
  with members:
    - "an efficient algorithm for partial order production"

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.DS(Data Structures and Algorithms)