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: "constant factor fpt approximation for capacitated k median"
  Abstract: Capacitated k-median is one of the few outstanding optimization problems for which the existence of a polynomial time constant factor approximation algorithm remains an open problem. In a series of recent papers algorithms producing solutions violating either the number of facilities or the capacity by a multiplicative factor were obtained. However, to produce solutions without violations appears to be hard and potentially requires different algorithmic techniques. Notably, if parameterized by the number of facilities $k$, the problem is also $W[2]$ hard, making the existence of an exact FPT algorithm unlikely. In this work we provide an FPT-time constant factor approximation algorithm preserving both cardinality and capacity of the facilities. The algorithm runs in time $2^{\mathcal{O}(k\log k)}n^{\mathcal{O}(1)}$ and achieves an approximation ratio of $7+\varepsilon$.

Connected through hyperedge HE-1 (induced by source paper "fair clustering with multiple colors"):
  with members:
    - "coresets for clustering with fairness constraints"
    - "fair regression quantitative definitions and reduction based algorithms"
    - "compositional fairness constraints for graph embeddings"
    - "scalable fair clustering"
    - "fair dimensionality reduction and iterative rounding for sdps"
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "privacy preserving clustering with constraints"
    - "a unified framework for clustering constrained data without locality property"

Connected through hyperedge HE-2 (induced by source paper "parameterized inapproximability for steiner orientation by gap amplification"):
  with members:
    - "tight fpt approximations for k median and k means"
    - "faster exact and approximate algorithms for k cut"
    - "from gap eth to fpt inapproximability clique dominating set and more"
    - "completely inapproximable monotone and antimonotone parameterized problems"
    - "almost polynomial ratio eth hardness of approximating densest k subgraph"
    - "a birthday repetition theorem and complexity of approximating dense csps"
    - "directed multicut is w 1 hard even for four terminal pairs"
    - "the parameterized complexity of k biclique"

Connected through hyperedge HE-3 (induced by source paper "fair algorithms for clustering"):
  with members:
    - "tight fpt approximations for k median and k means"
    - "scalable fair clustering"
    - "on the cost of essentially fair clusterings"
    - "privacy preserving clustering with constraints"
    - "a unified framework for clustering constrained data without locality property"
    - "fair clustering through fairlets"
    - "generalized center problems with outliers"
    - "classification with fairness constraints a meta algorithm with provable guarantees"

Connected through hyperedge HE-4 (induced by source paper "streaming balanced clustering"):
  with members:
    - "a constant parameterized approximation for hard capacitated k means"
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "nearly optimal dynamic k means clustering for high dimensional data"
    - "relative error tensor low rank approximation"
    - "clustering high dimensional dynamic data streams"
    - "distributed low rank approximation of implicit functions of a matrix"
    - "new frameworks for offline and streaming coreset constructions"
    - "low rank approximation with entrywise ell_1 norm error"

Connected through hyperedge HE-5 (induced by source paper "a constant fpt approximation algorithm for hard capacitated k means"):
  with members:
    - "greedy bi criteria approximations for k medians and k means"
    - "better guarantees for k means and euclidean k median by primal dual algorithms"
    - "local search yields a ptas for k means in doubling metrics"
    - "local search yields approximation schemes for k means and k median in euclidean and minor free metrics"
    - "on variants of k means clustering"
    - "an approximation algorithm for uniform capacitated k median problem with 1 epsilon capacity violation"
    - "improved and simplified inapproximability for k means"
    - "the hardness of approximation of euclidean k means"

Connected through hyperedge HE-6 (induced by source paper "inapproximability within w 1 the case of steiner orientation"):
  with members:
    - "tight fpt approximations for k median and k means"
    - "faster exact and approximate algorithms for k cut"
    - "from gap eth to fpt inapproximability clique dominating set and more"
    - "completely inapproximable monotone and antimonotone parameterized problems"
    - "directed multicut is w 1 hard even for four terminal pairs"
    - "the parameterized complexity of k biclique"
    - "steiner forest orientation problems"
    - "multicut is fpt"

Connected through hyperedge HE-7 (induced by source paper "a constant parameterized approximation for hard capacitated k means"):
  with members:
    - "greedy bi criteria approximations for k medians and k means"
    - "local search yields a ptas for k means in doubling metrics"
    - "local search yields approximation schemes for k means and k median in euclidean and minor free metrics"
    - "an approximation algorithm for uniform capacitated k median problem with 1 epsilon capacity violation"
    - "the hardness of approximation of euclidean k means"
    - "on uniform capacitated k median beyond the natural lp relaxation"

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)