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: "achieving non discrimination in prediction"
  Abstract: Discrimination-aware classification is receiving an increasing attention in data science fields. The pre-process methods for constructing a discrimination-free classifier first remove discrimination from the training data, and then learn the classifier from the cleaned data. However, they lack a theoretical guarantee for the potential discrimination when the classifier is deployed for prediction. In this paper, we fill this gap by mathematically bounding the probability of the discrimination in prediction being within a given interval in terms of the training data and classifier. We adopt the causal model for modeling the data generation mechanism, and formally defining discrimination in population, in a dataset, and in prediction. We obtain two important theoretical results: (1) the discrimination in prediction can still exist even if the discrimination in the training data is completely removed; and (2) not all pre-process methods can ensure non-discrimination in prediction even though they can achieve non-discrimination in the modified training data. Based on the results, we develop a two-phase framework for constructing a discrimination-free classifier with a theoretical guarantee. The experiments demonstrate the theoretical results and show the effectiveness of our two-phase framework.

Connected through hyperedge HE-1 (induced by source paper "hypoml visual analysis for hypothesis based evaluation of machine learning models"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "gan lab understanding complex deep generative models using interactive visual experimentation"
    - "manifold a model agnostic framework for interpretation and diagnosis of machine learning models"
    - "deeptracker visualizing the training process of convolutional neural networks"
    - "visual interpretability for deep learning a survey"
    - "understanding hidden memories of recurrent neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "towards better analysis of deep convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "on discrimination discovery and removal in ranked data using causal graph"):
  with members:
    - "fa ir a fair top k ranking algorithm"
    - "exposing the probabilistic causal structure of discrimination"
    - "achieving non discrimination in data release"
    - "measuring fairness in ranked outputs"
    - "a causal framework for discovering and removing direct and indirect discrimination"
    - "a new look at causal independence"
    - "fairness through awareness"

Connected through hyperedge HE-3 (induced by source paper "fairness aware classification criterion convexity and bounds"):
  with members:
    - "on fairness and calibration"
    - "spectral algorithms for computing fair support vector machines"
    - "equality of opportunity in supervised learning"
    - "achieving non discrimination in data release"
    - "inherent trade offs in the fair determination of risk scores"
    - "a causal framework for discovering and removing direct and indirect discrimination"
    - "satisfying real world goals with dataset constraints"

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)