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: "online learning with an unknown fairness metric"
  Abstract: We consider the problem of online learning in the linear contextual bandits setting, but in which there are also strong individual fairness constraints governed by an unknown similarity metric. These constraints demand that we select similar actions or individuals with approximately equal probability (arXiv:1104.3913), which may be at odds with optimizing reward, thus modeling settings where profit and social policy are in tension. We assume we learn about an unknown Mahalanobis similarity metric from only weak feedback that identifies fairness violations, but does not quantify their extent. This is intended to represent the interventions of a regulator who "knows unfairness when he sees it" but nevertheless cannot enunciate a quantitative fairness metric over individuals. Our main result is an algorithm in the adversarial context setting that has a number of fairness violations that depends only logarithmically on $T$, while obtaining an optimal $O(\sqrt{T})$ regret bound to the best fair policy.

Connected through hyperedge HE-1 (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"
    - "a survey on bias and fairness in machine learning"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "individual fairness revisited transferring techniques from adversarial robustness"):
  with members:
    - "metric learning for individual fairness"
    - "eliciting and enforcing subjective individual fairness"
    - "learning fair representations for kernel models"
    - "from soft classifiers to hard decisions how fair can we be"
    - "learning adversarially fair and transferable representations"
    - "the frontiers of fairness in machine learning"
    - "bias mitigation post processing for individual and group fairness"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-3 (induced by source paper "contextual search for general hypothesis classes"):
  with members:
    - "corrupted multidimensional binary search learning in the presence of irrational agents"
    - "contextual search via intrinsic volumes"
    - "bandits and experts in metric spaces"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "contextual bandits with similarity information"

Connected through hyperedge HE-4 (induced by source paper "achieving fairness in the stochastic multi armed bandit problem"):
  with members:
    - "advancing subgroup fairness via sleeping experts"
    - "fair logistic regression an adversarial perspective"
    - "fairness in recommendation ranking through pairwise comparisons"
    - "combinatorial sleeping bandits with fairness constraints"
    - "policy learning for fairness in ranking"
    - "equal opportunity in online classification with partial feedback"
    - "introduction to multi armed bandits"
    - "an algorithmic framework to control bias in bandit based personalization"

Connected through hyperedge HE-5 (induced by source paper "fair learning to rank from implicit feedback"):
  with members:
    - "fairness aware ranking in search recommendation systems with application to linkedin talent search"
    - "achieving fairness in the stochastic multi armed bandit problem"
    - "fair logistic regression an adversarial perspective"
    - "the few get richer a surprising consequence of popularity based rankings"
    - "intervention harvesting for context dependent examination bias estimation"
    - "fairness in recommendation ranking through pairwise comparisons"
    - "estimating position bias without intrusive interventions"
    - "policy learning for fairness in ranking"

Connected through hyperedge HE-6 (induced by source paper "fast low rank metric learning for large scale and high dimensional data"):
  with members:
    - "parts4feature learning 3d global features from generally semantic parts in multiple views"
    - "3dviewgraph learning global features for 3d shapes from a graph of unordered views with attention"
    - "l2g auto encoder understanding point clouds by local to global reconstruction with hierarchical self attention"
    - "multi angle point cloud vae unsupervised feature learning for 3d point clouds from multiple angles by joint self reconstruction and half to half prediction"
    - "y 2seq2seq cross modal representation learning for 3d shape and text by joint reconstruction and prediction of view and word sequences"
    - "view inter prediction gan unsupervised representation learning for 3d shapes by learning global shape memories to support local view predictions"
    - "point2sequence learning the shape representation of 3d point clouds with an attention based sequence to sequence network"
    - "opml a one pass closed form solution for online metric learning"

Connected through hyperedge HE-7 (induced by source paper "fair decisions despite imperfect predictions"):
  with members:
    - "consequential ranking algorithms and long term welfare"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "delayed impact of fair machine learning"
    - "a short term intervention for long term fairness in the labor market"
    - "from fair decision making to social equality"
    - "learning non discriminatory predictors"
    - "algorithmic decision making and the cost of fairness"
    - "fairness in reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "operationalizing individual fairness with pairwise fair representations"):
  with members:
    - "eliciting and enforcing subjective individual fairness"
    - "equity of attention amortizing individual fairness in rankings"
    - "a unified approach to quantifying algorithmic unfairness measuring individual group unfairness via inequality indices"
    - "fair clustering through fairlets"
    - "ifair learning individually fair data representations for algorithmic decision making"
    - "representation learning on graphs methods and applications"
    - "ranking with fairness constraints"
    - "algorithmic decision making and the cost of fairness"

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)