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: "fair and diverse dpp based data summarization"
  Abstract: Sampling methods that choose a subset of the data proportional to its diversity in the feature space are popular for data summarization. However, recent studies have noted the occurrence of bias (under- or over-representation of a certain gender or race) in such data summarization methods. In this paper we initiate a study of the problem of outputting a diverse and fair summary of a given dataset. We work with a well-studied determinantal measure of diversity and corresponding distributions (DPPs) and present a framework that allows us to incorporate a general class of fairness constraints into such distributions. Coming up with efficient algorithms to sample from these constrained determinantal distributions, however, suffers from a complexity barrier and we present a fast sampler that is provably good when the input vectors satisfy a natural property. Our experimental results on a real-world and an image dataset show that the diversity of the samples produced by adding fairness constraints is not too far from the unconstrained case, and we also provide a theoretical explanation of it.

Connected through hyperedge HE-1 (induced by source paper "interventions for ranking in the presence of implicit bias"):
  with members:
    - "fairness aware ranking in search recommendation systems with application to linkedin talent search"
    - "coresets for clustering with fairness constraints"
    - "toward controlling discrimination in online ad auctions"
    - "pairwise fairness for ranking and regression"
    - "classification with fairness constraints a meta algorithm with provable guarantees"
    - "fairness of exposure in rankings"
    - "multiwinner voting with fairness constraints"
    - "fa ir a fair top k ranking algorithm"

Connected through hyperedge HE-2 (induced by source paper "stable and fair classification"):
  with members:
    - "toward controlling discrimination in online ad auctions"
    - "ai fairness 360 an extensible toolkit for detecting understanding and mitigating unwanted algorithmic bias"
    - "generalization bounds for uniformly stable algorithms"
    - "classification with fairness constraints a meta algorithm with provable guarantees"
    - "a reductions approach to fair classification"
    - "on fairness and calibration"
    - "certified defenses for data poisoning attacks"
    - "mathematics of deep learning"

Connected through hyperedge HE-3 (induced by source paper "summarizing user generated textual content motivation and methods for fairness in algorithmic summaries"):
  with members:
    - "discrimination in the age of algorithms"
    - "equity of attention amortizing individual fairness in rankings"
    - "simplicity creates inequity implications for fairness stereotypes and interpretability"
    - "fair clustering through fairlets"
    - "equality of voice towards fair representation in crowdsourced top k recommendations"
    - "selection problems in the presence of implicit bias"
    - "a survey on neural network based summarization methods"
    - "text summarization techniques a brief survey"

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"
    - "clustering without over representation"
    - "on the cost of essentially fair clusterings"
    - "privacy preserving clustering with constraints"
    - "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 "flexible modeling of diversity with strongly log concave distributions"):
  with members:
    - "submodular maximization beyond non negativity guarantees fast algorithms and applications"
    - "log concave polynomials ii high dimensional walks and an fpras for counting bases of a matroid"
    - "gdpp learning diverse generations using determinantal point process"
    - "guess free maximization of submodular and linear sums"
    - "unbiased estimates for linear regression via volume sampling"
    - "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes"
    - "kronecker determinantal point processes"
    - "diversity networks neural network compression using determinantal point processes"

Connected through hyperedge HE-6 (induced by source paper "implicit diversity in image summarization"):
  with members:
    - "mitigating unwanted biases with adversarial learning"
    - "classification with fairness constraints a meta algorithm with provable guarantees"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "algorithmic decision making and the cost of fairness"
    - "deep convolutional neural networks for computer aided detection cnn architectures dataset characteristics and transfer learning"
    - "equality of opportunity in supervised learning"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "facenet a unified embedding for face recognition and clustering"

Connected through hyperedge HE-7 (induced by source paper "clustering with fairness constraints a flexible and scalable approach"):
  with members:
    - "scalable fair clustering"
    - "fair clustering through fairlets"
    - "the price of fair pca one extra dimension"
    - "fair coresets and streaming algorithms for fair k means clustering"
    - "scalable laplacian k modes"
    - "equality of opportunity in supervised learning"
    - "a submodular supermodular procedure with applications to discriminative structure learning"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-8 (induced by source paper "toward controlling discrimination in online ad auctions"):
  with members:
    - "discrimination through optimization how facebook s ad delivery can lead to biased outcomes"
    - "classification with fairness constraints a meta algorithm with provable guarantees"
    - "ranking with fairness constraints"
    - "multiwinner voting with fairness constraints"
    - "reducing controversy by connecting opposing views"
    - "discrimination in online ad delivery"
    - "bidding for representative allocations for display advertising"

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)