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 fairness constraints a flexible and scalable approach"
  Abstract: This study investigates a general variational formulation of fair clustering, which can integrate fairness constraints with a large class of clustering objectives. Unlike the existing methods, our formulation can impose any desired (target) demographic proportions within each cluster. Furthermore, it enables to control the trade-off between fairness and the clustering objective. We derive an auxiliary function (tight upper bound) of our KL-based fairness penalty via its concave-convex decomposition and Lipschitz-gradient property. Our upper bound can be optimized jointly with various clustering objectives, including both prototype-based such as K-means and graph-based such as Normalized Cut. Interestingly, at each iteration, our general fair-clustering algorithm performs an independent update for each assignment variable, while guaranteeing convergence. Therefore, it can be easily distributed for large-scale data sets. Such scalability is important as it enables to explore different trade-off levels between fairness and clustering objectives. Unlike existing fairness-constrained spectral clustering, our formulation does not need storing an affinity matrix and computing its eigenvalue decomposition. Moreover, unlike existing prototype-based methods, our experiments reveal that fairness does not come at a significant cost of the clustering objective. In fact, several of our tests showed that our fairness penalty helped to avoid weak local minima of the clustering objective (i.e., with fairness, we obtained better clustering objectives). We demonstrate the flexibility and scalability of our algorithm with comprehensive evaluations over both synthetic and real world data sets, many of which are much larger than those used in recent fair-clustering methods.

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 "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 without over representation"
    - "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.LG(Machine Learning)