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: "compositional fairness constraints for graph embeddings"
  Abstract: Learning high-quality node embeddings is a key building block for machine learning models that operate on graph data, such as social networks and recommender systems. However, existing graph embedding techniques are unable to cope with fairness constraints, e.g., ensuring that the learned representations do not correlate with certain attributes, such as age or gender. Here, we introduce an adversarial framework to enforce fairness constraints on graph embeddings. Our approach is compositional---meaning that it can flexibly accommodate different combinations of fairness constraints during inference. For instance, in the context of social recommendations, our framework would allow one user to request that their recommendations are invariant to both their age and gender, while also allowing another user to request invariance to just their age. Experiments on standard knowledge graph and recommender system benchmarks highlight the utility of our proposed framework.

Connected through hyperedge HE-1 (induced by source paper "algorithmic fairness"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "fairface face attribute dataset for balanced race gender and age"
    - "fair regression quantitative definitions and reduction based algorithms"
    - "putting fairness principles into practice challenges metrics and improvements"
    - "generative adversarial networks for mitigating biases in machine learning systems"
    - "improved adversarial learning for fair classification"
    - "mathematical notions vs human perception of fairness a descriptive approach to fairness for machine learning"
    - "scalable fair clustering"

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"
    - "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"
    - "a unified framework for clustering constrained data without locality property"

Connected through hyperedge HE-3 (induced by source paper "debayes a bayesian method for debiasing network embeddings"):
  with members:
    - "monet debiasing graph embeddings via the metadata orthogonal training unit"
    - "a survey on bias and fairness in machine learning"
    - "exploring author gender in book rating and recommendation"
    - "representation learning on graphs methods and applications"
    - "a survey on network embedding"
    - "learning non discriminatory predictors"
    - "equality of opportunity in supervised learning"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-4 (induced by source paper "a survey on bias and fairness in machine learning"):
  with members:
    - "debiasing community detection the importance of lowly connected nodes"
    - "50 years of test un fairness lessons for machine learning"
    - "diversity in faces"
    - "on measuring social biases in sentence encoders"
    - "equalizing gender biases in neural machine translation with word embeddings techniques"
    - "fair regression quantitative definitions and reduction based algorithms"
    - "proportionally fair clustering"
    - "a framework for understanding unintended consequences of machine learning"

Connected through hyperedge HE-5 (induced by source paper "fairness in deep learning a computational perspective"):
  with members:
    - "learning credible deep neural networks with rationale regularization"
    - "auditing imagenet towards a model driven framework for annotating demographic attributes of large scale image datasets"
    - "putting fairness principles into practice challenges metrics and improvements"
    - "incorporating priors with feature attribution on text classification"
    - "deep learning for face recognition pride or prejudiced"
    - "on attribution of recurrent neural network predictions via additive decomposition"
    - "flexibly fair representation learning by disentanglement"
    - "xlnet generalized autoregressive pretraining for language understanding"

Connected through hyperedge HE-6 (induced by source paper "does gender matter towards fairness in dialogue systems"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "a survey on bias and fairness in machine learning"
    - "scalable fair clustering"
    - "identifying and reducing gender bias in word level language models"
    - "build it break it fix it for dialogue safety robustness from adversarial human attack"
    - "say what i want towards the dark side of neural dialogue models"
    - "assessing gender bias in machine translation a case study with google translate"
    - "gender bias in neural natural language processing"

Connected through hyperedge HE-7 (induced by source paper "measuring social bias in knowledge graph embeddings"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "evaluating gender bias in machine translation"
    - "black is to criminal as caucasian is to police detecting and removing multiclass bias in word embeddings"
    - "ernie enhanced language representation with informative entities"
    - "evaluating the underlying gender bias in contextualized word embeddings"
    - "knowledge enhanced contextual word representations"

Connected through hyperedge HE-8 (induced by source paper "monet debiasing graph embeddings via the metadata orthogonal training unit"):
  with members:
    - "global vectors for node representations"
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "graph convolutional neural networks for web scale recommender systems"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "a tutorial on network embeddings"
    - "enhancing network embedding with auxiliary information an explicit matrix factorization perspective"
    - "a survey on network embedding"
    - "the ground truth about metadata and community detection in networks"

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)