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: "improved adversarial learning for fair classification"
  Abstract: Motivated by concerns that machine learning algorithms may introduce significant bias in classification models, developing fair classifiers has become an important problem in machine learning research. One important paradigm towards this has been providing algorithms for adversarially learning fair classifiers (Zhang et al., 2018; Madras et al., 2018). We formulate the adversarial learning problem as a multi-objective optimization problem and find the fair model using gradient descent-ascent algorithm with a modified gradient update step, inspired by the approach of Zhang et al., 2018. We provide theoretical insight and guarantees that formalize the heuristic arguments presented previously towards taking such an approach. We test our approach empirically on the Adult dataset and synthetic datasets and compare against state of the art algorithms (Celis et al., 2018; Zhang et al., 2018; Zafar et al., 2017). The results show that our models and algorithms have comparable or better accuracy than other algorithms while performing better in terms of fairness, as measured using statistical rate or false discovery rate.

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"
    - "compositional fairness constraints for graph embeddings"
    - "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 "think locally act globally federated learning with local and global representations"):
  with members:
    - "one shot federated learning"
    - "towards federated learning at scale system design"
    - "quantized epoch sgd for communication efficient distributed learning"
    - "agnostic federated learning"
    - "learning fair representations via an adversarial framework"
    - "gender bias in contextualized word embeddings"
    - "federated machine learning concept and applications"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"

Connected through hyperedge HE-3 (induced by source paper "generative adversarial networks for mitigating biases in machine learning systems"):
  with members:
    - "training generative adversarial networks via primal dual subgradient methods a lagrangian perspective on gan"
    - "mitigating unwanted biases with adversarial learning"
    - "learning adversarially fair and transferable representations"
    - "a reductions approach to fair classification"
    - "on fairness and calibration"
    - "learning non discriminatory predictors"
    - "improved training of wasserstein gans"
    - "nips 2016 tutorial generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "fairness for robust log loss classification"):
  with members:
    - "training well generalizing classifiers for fairness metrics and other data dependent constraints"
    - "mitigating unwanted biases with adversarial learning"
    - "learning adversarially fair and transferable representations"
    - "fairness of exposure in rankings"
    - "a reductions approach to fair classification"
    - "fairgan fairness aware generative adversarial networks"
    - "penalizing unfairness in binary classification"
    - "on fairness and calibration"

Connected through hyperedge HE-5 (induced by source paper "leveraging semi supervised learning for fairness using neural networks"):
  with members:
    - "achieving fairness through adversarial learning an application to recidivism prediction"
    - "a neural network framework for fair classifier"
    - "mitigating unwanted biases with adversarial learning"
    - "learning adversarially fair and transferable representations"
    - "fairness through computationally bounded awareness"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "a reductions approach to fair classification"
    - "on fairness and calibration"

Connected through hyperedge HE-6 (induced by source paper "fair logistic regression an adversarial perspective"):
  with members:
    - "training well generalizing classifiers for fairness metrics and other data dependent constraints"
    - "mitigating unwanted biases with adversarial learning"
    - "classification with fairness constraints a meta algorithm with provable guarantees"
    - "learning adversarially fair and transferable representations"
    - "fairness of exposure in rankings"
    - "fairgan fairness aware generative adversarial networks"
    - "penalizing unfairness in binary classification"
    - "on fairness and calibration"

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)