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: "maximum volume subset selection for anchored boxes"
  Abstract: Let B be a set of n axis-parallel boxes in d-dimensions such that each box has a corner at the origin and the other corner in the positive quadrant, and let k be a positive integer. We study the problem of selecting k boxes in B that maximize the volume of the union of the selected boxes. The research is motivated by applications in skyline queries for databases and in multicriteria optimization, where the problem is known as the hypervolume subset selection problem. It is known that the problem can be solved in polynomial time in the plane, while the best known algorithms in any dimension d>2 enumerate all size-k subsets. We show that:#R##N##R##N#* The problem is NP-hard already in 3 dimensions.#R##N##R##N#* In 3 dimensions, we break the enumeration of all size-k subsets, by providing an n^O(sqrt(k)) algorithm.#R##N##R##N#* For any constant dimension d, we give an efficient polynomial-time approximation scheme.

Connected through hyperedge HE-1 (induced by source paper "polynomial algorithms for p dispersion problems in a 2d pareto front"):
  with members:
    - "faster k medoids clustering improving the pam clara and clarans algorithms"
    - "fast exact k means k medians and bregman divergence clustering in 1d"

Connected through hyperedge HE-2 (induced by source paper "fpt algorithms for diverse collections of hitting sets"):
  with members:
    - "diversity in combinatorial optimization"
    - "preparing for the unexpected diversity improves planning resilience in evolutionary algorithms"
    - "discrepancy based evolutionary diversity optimization"
    - "faster deterministic feedback vertex set"
    - "cutset sampling with likelihood weighting"
    - "approximation algorithms for secondary spectrum auctions"
    - "minimal change integrity maintenance using tuple deletions"

Connected through hyperedge HE-3 (induced by source paper "planar p center problems are solvable in polynomial time when clustering a pareto front"):
  with members:
    - "clustering in a 2 dimensional pareto front the p median and p center problems are polynomially solvable"
    - "fast exact k means k medians and bregman divergence clustering in 1d"
    - "computing k centers on a line"

Connected through hyperedge HE-4 (induced by source paper "clustering in a 2 dimensional pareto front the p median and p center problems are polynomially solvable"):
  with members:
    - "fast exact k means k medians and bregman divergence clustering in 1d"
    - "optimal interval clustering application to bregman clustering and statistical mixture learning"

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.CG(Computational Geometry)