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: "building a good team secretary problems and the supermodular degree"
  Abstract: In the Secretary Problem, one has to hire the best among n candidates. The candidates are interviewed, one at a time, at a random order, and one has to decide on the spot, whether to hire a candidate or continue interviewing. It is well known that the best candidate can be hired with a probability of 1/e (Dynkin, 1963). Recent works extend this problem to settings in which multiple candidates can be hired, subject to some constraint. Here, one wishes to hire a set of candidates maximizing a given set function. #R##N#Almost all extensions considered in the literature assume the objective set function is either linear or submodular. Unfortunately, real world functions might not have either of these properties. Consider, for example, a scenario where one hires researchers for a project. Indeed, it can be that some researchers can substitute others for that matter. However, it can also be that some combinations of researchers result in synergy (see, e.g, Woolley et al., Science 2010, for a research about collective intelligence). The first phenomenon can be modeled by a submoudlar set function, while the latter cannot. #R##N#In this work, we study the secretary problem with an arbitrary non-negative monotone function, subject to a general matroid constraint. It is not difficult to prove that, generally, only very poor results can be obtained for this class of objective functions. We tackle this hardness by combining the following: 1.Parametrizing our algorithms by the supermodular degree of the objective function (defined by Feige and Izsak, ITCS 2013), which, roughly speaking, measures the distance of a function from being submodular. 2.Suggesting an (arguably) natural model that permits approximation guarantees that are polynomial in the supermodular degree (as opposed to the standard model which allows only exponential guarantees).

Connected through hyperedge HE-1 (induced by source paper "beyond submodular maximization"):
  with members:
    - "distributed maximization of submodular plus diversity functions for multi label feature selection on huge datasets"
    - "weakly submodular maximization beyond cardinality constraints does randomization help greedy"
    - "online submodular welfare maximization greedy beats 1 2 in random order"
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "local search for max sum diversification"
    - "max sum diversification monotone submodular functions and semi metric spaces"
    - "max sum diversity via convex programming"
    - "a new framework for distributed submodular maximization"

Connected through hyperedge HE-2 (induced by source paper "an improved algorithm for the submodular secretary problem with a cardinality constraint"):
  with members:
    - "combinatorial prophet inequalities"
    - "constrained submodular maximization via a non symmetric technique"
    - "submodular secretary problems cardinality matching and linear constraints"
    - "the submodular secretary problem goes linear"
    - "the simulated greedy algorithm for several submodular matroid secretary problems"
    - "constrained non monotone submodular maximization offline and secretary algorithms"

Connected through hyperedge HE-3 (induced by source paper "the warm starting sequential selection problem and its multi round extension"):
  with members:
    - "strong algorithms for the ordinal matroid secretary problem"
    - "learning in the repeated secretary problem"
    - "a framework for the secretary problem on the intersection of matroids"
    - "think eternally improved algorithms for the temp secretary problem and extensions"
    - "the temp secretary problem"

Connected through hyperedge HE-4 (induced by source paper "capturing complementarity in set functions by going beyond submodularity subadditivity"):
  with members:
    - "network essence pagerank completion and centrality conforming markov chains"
    - "simple mechanisms for agents with complements"
    - "a simple and approximately optimal mechanism for a buyer with complements"
    - "constrained monotone function maximization and the supermodular degree"
    - "a unifying hierarchy of valuations with complements and substitutes"
    - "combinatorial auctions via posted prices"
    - "combinatorial auctions with restricted complements"
    - "algorithmic pricing via virtual valuations"

Connected through hyperedge HE-5 (induced by source paper "prophets and secretaries with overbooking"):
  with members:
    - "think eternally improved algorithms for the temp secretary problem and extensions"
    - "combinatorial prophet inequalities"
    - "secretary problems with non uniform arrival order"
    - "combinatorial auctions via posted prices"
    - "advances on matroid secretary problems free order model and laminar case"
    - "bayesian combinatorial auctions expanding single buyer mechanisms to many buyers"
    - "secretary problems with convex costs"
    - "buyback problem approximate matroid intersection with cancellation costs"

Connected through hyperedge HE-6 (induced by source paper "the multi round sequential selection problem"):
  with members:
    - "strong algorithms for the ordinal matroid secretary problem"
    - "learning in the repeated secretary problem"
    - "a framework for the secretary problem on the intersection of matroids"
    - "a simple o log log mathrm rank competitive algorithm for the matroid secretary problem"

Connected through hyperedge HE-7 (induced by source paper "weakly submodular maximization beyond cardinality constraints does randomization help greedy"):
  with members:
    - "guarantees for greedy maximization of non submodular functions with applications"
    - "submodular variational inference for network reconstruction"
    - "why should i trust you explaining the predictions of any classifier"
    - "constrained submodular maximization via a non symmetric technique"
    - "constrained submodular maximization beyond 1 e"
    - "constrained monotone function maximization and the supermodular degree"
    - "lazier than lazy greedy"
    - "maximizing symmetric submodular functions"

Connected through hyperedge HE-8 (induced by source paper "cooperative games with bounded dependency degree"):
  with members:
    - "simple mechanisms for agents with complements"
    - "a unifying hierarchy of valuations with complements and substitutes"
    - "coalition structure generation over graphs"
    - "on the complexity of core kernel and bargaining set"

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)