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 without over representation"
  Abstract: In this paper we consider clustering problems in which each point is endowed with a color. The goal is to cluster the points to minimize the classical clustering cost but with the additional constraint that no color is over-represented in any cluster. This problem is motivated by practical clustering settings, e.g., in clustering news articles where the color of an article is its source, it is preferable that no single news source dominates any cluster. For the most general version of this problem, we obtain an algorithm that has provable guarantees of performance; our algorithm is based on finding a fractional solution using a linear program and rounding the solution subsequently. For the special case of the problem where no color has an absolute majority in any cluster, we obtain a simpler combinatorial algorithm also with provable guarantees. Experiments on real-world data shows that our algorithms are effective in finding good clustering without over-representation.

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 "individual fairness for k clustering"):
  with members:
    - "coresets for clustering with fairness constraints"
    - "r enyi fair inference"
    - "a survey on bias and fairness in machine learning"
    - "fairness in clustering with multiple sensitive attributes"
    - "a center in your neighborhood fairness in facility location"
    - "scalable fair clustering"
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "on the cost of essentially fair clusterings"

Connected through hyperedge HE-3 (induced by source paper "how to solve fair k center in massive data models"):
  with members:
    - "a constant approximation for colorful k center"
    - "fair clustering through fairlets"
    - "small space stream summary for matroid center"
    - "deep learning face attributes in the wild"
    - "matroid and knapsack center problems"

Connected through hyperedge HE-4 (induced by source paper "coresets for clustering with fairness constraints"):
  with members:
    - "toward controlling discrimination in online ad auctions"
    - "principal fairness removing bias via projections"
    - "scalable fair clustering"
    - "on the cost of essentially fair clusterings"
    - "privacy preserving clustering with constraints"
    - "fair and diverse dpp based data summarization"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "fair clustering through fairlets"

Connected through hyperedge HE-5 (induced by source paper "fairness in clustering with multiple sensitive attributes"):
  with members:
    - "proportionally fair clustering"
    - "principal fairness removing bias via projections"
    - "fair algorithms for clustering"
    - "towards fair deep clustering with multi state protected variables"
    - "scalable fair clustering"
    - "clustering with fairness constraints a flexible and scalable approach"
    - "privacy preserving clustering with constraints"
    - "convex formulations for fair principal component analysis"

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)