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: "proxy fairness"
  Abstract: We consider the problem of improving fairness when one lacks access to a dataset labeled with protected groups, making it difficult to take advantage of strategies that can improve fairness but require protected group labels, either at training or runtime. To address this, we investigate improving fairness metrics for proxy groups, and test whether doing so results in improved fairness for the true sensitive groups. Results on benchmark and real-world datasets demonstrate that such a proxy fairness strategy can work well in practice. However, we caution that the effectiveness likely depends on the choice of fairness metric, as well as how aligned the proxy groups are with the true protected groups in terms of the constrained model parameters.

Connected through hyperedge HE-1 (induced by source paper "balancing competing objectives with noisy data score based classifiers for welfare aware machine learning"):
  with members:
    - "a longitudinal analysis of youtube s promotion of conspiracy videos"
    - "the disparate equilibria of algorithmic decision making when individuals invest rationally"
    - "fairness and utilization in allocating resources with uncertain demand"
    - "fair classification and social welfare"
    - "noise tolerant fair classification"
    - "delayed impact of fair machine learning"
    - "welfare and distributional impacts of fair classification"
    - "a flexible framework for multi objective bayesian optimization using random scalarizations"

Connected through hyperedge HE-2 (induced by source paper "fair learning with private demographic data"):
  with members:
    - "differential privacy has disparate impact on model accuracy"
    - "the cost of a reductions approach to private fair optimization"
    - "noise tolerant fair classification"
    - "training well generalizing classifiers for fairness metrics and other data dependent constraints"
    - "differentially private fair learning"
    - "a reductions approach to fair classification"
    - "equality of opportunity in supervised learning"
    - "extremal mechanisms for local differential privacy"

Connected through hyperedge HE-3 (induced by source paper "robust optimization for fairness with noisy protected groups"):
  with members:
    - "pairwise fairness for ranking and regression"
    - "two player games for efficient non convex constrained optimization"
    - "equality of opportunity in supervised learning"
    - "satisfying real world goals with dataset constraints"
    - "fairness through awareness"

Connected through hyperedge HE-4 (induced by source paper "noise tolerant fair classification"):
  with members:
    - "agnostic federated learning"
    - "fairness risk measures"
    - "training well generalizing classifiers for fairness metrics and other data dependent constraints"
    - "a unified approach to quantifying algorithmic unfairness measuring individual group unfairness via inequality indices"
    - "ifair learning individually fair data representations for algorithmic decision making"
    - "a moral framework for understanding of fair ml through economic models of equality of opportunity"
    - "differentially private fair learning"
    - "fairness through computationally bounded awareness"

Connected through hyperedge HE-5 (induced by source paper "transfer of machine learning fairness across domains"):
  with members:
    - "putting fairness principles into practice challenges metrics and improvements"
    - "mitigating unwanted biases with adversarial learning"
    - "learning adversarially fair and transferable representations"
    - "a reductions approach to fair classification"
    - "towards robust and privacy preserving text representations"
    - "data decisions and theoretical implications when adversarially learning fair representations"
    - "equality of opportunity in supervised learning"
    - "domain separation networks"

Connected through hyperedge HE-6 (induced by source paper "what s in a name reducing bias in bios without access to protected attributes"):
  with members:
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "mitigating unwanted biases with adversarial learning"
    - "what are the biases in my word embedding"
    - "semantics derived automatically from language corpora contain human like biases"
    - "equality of opportunity in supervised learning"
    - "enriching word vectors with subword information"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "adam a method for stochastic optimization"

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)