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: "combinatorial determinantal point processes"
  Abstract: Determinantal Point Processes (DPPs) are probabilistic models of repulsion that originate in quantum physics and random matrix theory and have been of recent interest in computer science. DPPs define distributions over subsets of a given ground set, and exhibit interesting properties such as negative correlation. When applied to kernel methods in machine learning, DPPs give rise to an efficient algorithm to select a small, diverse sample of the given data. Kulesza and Taskar [KT12] posed a natural open question: Can we sample from DPPs when there are additional constraints on the allowable subsets? #R##N#In this paper, we study the complexity of sampling from DPPs over combinatorially-constrained families of subsets and present several connections and applications. We start by showing that it is at least as hard to sample from combinatorial DPPs as it is to compute the mixed discriminant of a tuple of positive semidefinite matrices. Subsequently, we give a polynomial time algorithm for sampling from combinatorial DPPs with a constant number of linear constraints; thus, we make significant progress towards answering the question of [KT12]. These results can lead to several non-trivial applications; e.g., we show how to derandomize a result of Nikolov and Singh [NS16] for maximizing subdeterminants under (a constant number of) partition constraints and -- motivated by making the [MSS13] proof for the Kadison-Singer problem algorithmic -- give an algorithm to compute the higher-order coefficients of mixed characteristic polynomials.

Connected through hyperedge HE-1 (induced by source paper "real stable polynomials and matroids optimization and counting"):
  with members:
    - "how to be fair and diverse"
    - "nash social welfare matrix permanent and stable polynomials"
    - "on largest volume simplices and sub determinants"
    - "randomized rounding for the largest simplex problem"
    - "entropy optimization and counting"
    - "efficient volume sampling for row column subset selection"

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)