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: "a survey on bias and fairness in machine learning"
  Abstract: With the widespread use of AI systems and applications in our everyday lives, it is important to take fairness issues into consideration while designing and engineering these types of systems. Such systems can be used in many sensitive environments to make important and life-changing decisions; thus, it is crucial to ensure that the decisions do not reflect discriminatory behavior toward certain groups or populations. We have recently seen work in machine learning, natural language processing, and deep learning that addresses such challenges in different subdomains. With the commercialization of these systems, researchers are becoming aware of the biases that these applications can contain and have attempted to address them. In this survey we investigated different real-world applications that have shown biases in various ways, and we listed different sources of biases that can affect AI applications. We then created a taxonomy for fairness definitions that machine learning researchers have defined in order to avoid the existing bias in AI systems. In addition to that, we examined different domains and subdomains in AI showing what researchers have observed with regard to unfair outcomes in the state-of-the-art methods and how they have tried to address them. There are still many future directions and solutions that can be taken to mitigate the problem of bias in AI systems. We are hoping that this survey will motivate researchers to tackle these issues in the near future by observing existing work in their respective fields.

Connected through hyperedge HE-1 (induced by source paper "nestedvae isolating common factors via weak supervision"):
  with members:
    - "a two step disentanglement method"
    - "emerging disentanglement in auto encoder based unsupervised image content transfer"
    - "diversity in faces"
    - "toward gender inclusive coreference resolution"
    - "robust ordinal vae employing noisy pairwise comparisons for disentanglement"
    - "weakly supervised disentanglement with guarantees"
    - "contrastive variational autoencoder enhances salient features"
    - "weakly supervised disentanglement by pairwise similarities"

Connected through hyperedge HE-2 (induced by source paper "demographic bias in biometrics a survey on an emerging challenge"):
  with members:
    - "post comparison mitigation of demographic bias in face recognition using fair score normalization"
    - "50 years of test un fairness lessons for machine learning"
    - "diversity in faces"
    - "fairface face attribute dataset for balanced race gender and age"
    - "an experimental evaluation of covariates effects on unconstrained face verification"
    - "sensitivenets learning agnostic representations with application to face recognition"
    - "some research problems in biometrics the future beckons"
    - "mitigate bias in face recognition using skewness aware reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "ai safety state of the field through quantitative lens"):
  with members:
    - "fairness in learning based sequential decision algorithms a survey"
    - "dave i can assure you that it s going to be all right a definition case for and survey of algorithmic assurances in human autonomy trust relationships"
    - "explainability in human agent systems"
    - "classification schemas for artificial intelligence failures"
    - "explainable artificial intelligence xai concepts taxonomies opportunities and challenges toward responsible ai"
    - "machine learning testing survey landscapes and horizons"
    - "unexplainability and incomprehensibility of artificial intelligence"
    - "from what to how an initial review of publicly available ai ethics tools methods and research to translate principles into practices"

Connected through hyperedge HE-4 (induced by source paper "fairness in learning based sequential decision algorithms a survey"):
  with members:
    - "achieving fairness in the stochastic multi armed bandit problem"
    - "fair contextual multi armed bandits theory and experiments"
    - "on the long term impact of algorithmic decision policies effort unfairness and feature segregation through social learning"
    - "the disparate equilibria of algorithmic decision making when individuals invest rationally"
    - "fairness with dynamics"
    - "group retention when using machine learning in sequential decision making the interplay between user dynamics and fairness"
    - "combinatorial sleeping bandits with fairness constraints"
    - "downstream effects of affirmative action"

Connected through hyperedge HE-5 (induced by source paper "it s not what machines can learn it s what we cannot teach"):
  with members:
    - "mipaal mixed integer program as a layer"
    - "generating difficult sat instances by preventing triangles"
    - "satnet bridging deep learning and logical reasoning using a differentiable satisfiability solver"
    - "hidden stratification causes clinically meaningful failures in machine learning for medical imaging"
    - "guiding high performance sat solvers with unsat core predictions"
    - "deep learning for symbolic mathematics"
    - "the use of deep learning for symbolic integration a review of lample and charton 2019"
    - "mixmatch a holistic approach to semi supervised learning"

Connected through hyperedge HE-6 (induced by source paper "individual fairness for k clustering"):
  with members:
    - "coresets for clustering with fairness constraints"
    - "r enyi fair inference"
    - "fairness in clustering with multiple sensitive attributes"
    - "a center in your neighborhood fairness in facility location"
    - "scalable fair clustering"
    - "clustering without over representation"
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "on the cost of essentially fair clusterings"

Connected through hyperedge HE-7 (induced by source paper "fairness by explicability and adversarial shap learning"):
  with members:
    - "counterfactual fairness removing direct effects through regularization"
    - "explaining machine learning classifiers through diverse counterfactual explanations"
    - "a framework for understanding unintended consequences of machine learning"
    - "explainable ai for trees from local explanations to global understanding"
    - "a neural network framework for fair classifier"
    - "discriminative but not discriminatory a comparison of fairness definitions under different worldviews"
    - "mitigating unwanted biases with adversarial learning"
    - "classification with fairness constraints a meta algorithm with provable guarantees"

Connected through hyperedge HE-8 (induced by source paper "fair active learning"):
  with members:
    - "stable and fair classification"
    - "a nutritional label for rankings"
    - "achieving fairness through adversarial learning an application to recidivism prediction"
    - "assessing and remedying coverage for a given dataset"
    - "classification with fairness constraints a meta algorithm with provable guarantees"
    - "a reductions approach to fair classification"
    - "active fairness in algorithmic decision making"
    - "bias disparity in recommendation systems"

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)