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: "clustering with diversity"
  Abstract: We consider the {\em clustering with diversity} problem: given a set of colored points in a metric space, partition them into clusters such that each cluster has at least $\ell$ points, all of which have distinct colors. #R##N#We give a 2-approximation to this problem for any $\ell$ when the objective is to minimize the maximum radius of any cluster. We show that the approximation ratio is optimal unless $\mathbf{P=NP}$, by providing a matching lower bound. Several extensions to our algorithm have also been developed for handling outliers. This problem is mainly motivated by applications in privacy-preserving data publication.

Connected through hyperedge HE-1 (induced by source paper "fair correlation clustering"):
  with members:
    - "coresets for clustering with fairness constraints"
    - "summarizing user generated textual content motivation and methods for fairness in algorithmic summaries"
    - "improved algorithms for correlation clustering with local objectives"
    - "near neighbor who is the fairest of them all"
    - "correlation clustering with adaptive similarity queries"
    - "fair algorithms for clustering"
    - "scalable fair clustering"
    - "achieving fairness in determining medicaid eligibility through fairgroup construction"

Connected through hyperedge HE-2 (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"
    - "constant factor fpt approximation for capacitated k median"

Connected through hyperedge HE-3 (induced by source paper "clustering without over representation"):
  with members:
    - "fair algorithms for clustering"
    - "scalable fair clustering"
    - "on the cost of essentially fair clusterings"
    - "privacy preserving clustering with constraints"
    - "fair clustering through fairlets"
    - "language modeling by clustering with word embeddings for text readability assessment"
    - "ranking with fairness constraints"
    - "multiwinner voting with fairness constraints"

Connected through hyperedge HE-4 (induced by source paper "fully dynamic hierarchical diameter k clustering and k center"):
  with members:
    - "the non uniform k center problem"
    - "fast hierarchical clustering and other applications of dynamic closest pairs"

Connected through hyperedge HE-5 (induced by source paper "a unified framework for clustering constrained data without locality property"):
  with members:
    - "faster balanced clusterings in high dimension"
    - "faster algorithms for the constrained k means problem"
    - "the hardness of approximation of euclidean k means"
    - "net and prune a linear time algorithm for euclidean distance problems"
    - "parallel algorithms for geometric graph problems"
    - "a constant factor approximation algorithm for fault tolerant k median"
    - "fault tolerant clustering revisited"
    - "fast clustering with lower bounds no customer too far no shop too small"

Connected through hyperedge HE-6 (induced by source paper "on the cost of essentially fair clusterings"):
  with members:
    - "fair clustering through fairlets"
    - "constant approximation for k median and k means with outliers via iterative rounding"
    - "on uniform capacitated k median beyond the natural lp relaxation"
    - "an improved approximation for k median and positive correlation in budgeted optimization"
    - "approximation algorithms for hard capacitated k facility location problems"
    - "fairness through awareness"

Connected through hyperedge HE-7 (induced by source paper "privacy preserving clustering with constraints"):
  with members:
    - "capacitated center problems with two sided bounds and outliers"

Connected through hyperedge HE-8 (induced by source paper "ensemble based discovery of disjoint overlapping and fuzzy community structures in networks"):
  with members:
    - "ensemble based algorithms to detect disjoint and overlapping communities in networks"
    - "permanence and community structure in complex networks"
    - "metrics for community analysis a survey"
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "personalized pagerank solution paths"
    - "leveraging disjoint communities for detecting overlapping community structure"
    - "yasca a collective intelligence approach for community detection in complex networks"
    - "overlapping community detection in networks the state of the art and comparative study"

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)