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: "monotonic calibrated interpolated look up tables"
  Abstract: Real-world machine learning applications may require functions that are fast-to-evaluate and interpretable. In particular, guaranteed monotonicity of the learned function can be critical to user trust. We propose meeting these goals for low-dimensional machine learning problems by learning flexible, monotonic functions using calibrated interpolated look-up tables. We extend the structural risk minimization framework of lattice regression to train monotonic look-up tables by solving a convex problem with appropriate linear inequality constraints. In addition, we propose jointly learning interpretable calibrations of each feature to normalize continuous features and handle categorical or missing data, at the cost of making the objective non-convex. We address large-scale learning through parallelization, mini-batching, and propose random sampling of additive regularizer terms. Case studies with real-world problems with five to sixteen features and thousands to millions of training samples demonstrate the proposed monotonic functions can achieve state-of-the-art accuracy on practical problems while providing greater transparency to users.

Connected through hyperedge HE-1 (induced by source paper "monotonic cardinality estimation of similarity selection a deep learning approach"):
  with members:
    - "neo a learned query optimizer"
    - "selectivity estimation with deep likelihood models"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "an empirical analysis of deep learning for cardinality estimation"
    - "multi attribute selectivity estimation using deep learning"
    - "an end to end learning based cost estimator"
    - "learned cardinalities estimating correlated joins with deep learning"
    - "learning state representations for query optimization with deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "deontological ethics by monotonicity shape constraints"):
  with members:
    - "optimization with non differentiable constraints with applications to fairness recall churn and other goals"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "fairness of exposure in rankings"
    - "two player games for efficient non convex constrained optimization"
    - "a reductions approach to fair classification"
    - "a convex framework for fair regression"
    - "ranking with fairness constraints"
    - "fairness in machine learning lessons from political philosophy"

Connected through hyperedge HE-3 (induced by source paper "supervised quantile normalization for low rank matrix factorization"):
  with members:
    - "unconstrained monotonic neural networks"
    - "computational optimal transport complexity by accelerated gradient descent is better than by sinkhorn s algorithm"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "exact matrix completion via convex optimization"
    - "a direct formulation for sparse pca using semidefinite programming"

Connected through hyperedge HE-4 (induced by source paper "explanation in human ai systems a literature meta review synopsis of key ideas and publications and bibliography for explainable ai"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "ted teaching ai to explain its decisions"
    - "explaining explanations an overview of interpretability of machine learning"
    - "deep learning a critical appraisal"
    - "how do humans understand explanations from machine learning systems an evaluation of the human interpretability of explanation"
    - "explainable artificial intelligence xai the goodness criteria and the grasp ability test"
    - "interpretable explorable approximations of black box models"
    - "explaining the unexplained a class enhanced attentive response clear approach to understanding deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "unconstrained monotonic neural networks"):
  with members:
    - "sum of squares polynomial flow"
    - "neural ordinary differential equations"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "density estimation using real nvp"
    - "wavenet a generative model for raw audio"
    - "made masked autoencoder for distribution estimation"
    - "nice non linear independent components estimation"

Connected through hyperedge HE-6 (induced by source paper "signal combination for language identification"):
  with members:
    - "a fast compact accurate model for language identification of codemixed text"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "processing of missing data by neural networks"):
  with members:
    - "gain missing data imputation using generative adversarial nets"
    - "high resolution image inpainting using multi scale neural patch synthesis"
    - "semantic image inpainting with deep generative models"
    - "context encoders feature learning by inpainting"
    - "generalized rbf kernel for incomplete data"
    - "classification with low rank and missing data"

Connected through hyperedge HE-8 (induced by source paper "interpretable set functions"):
  with members:
    - "deep sets"
    - "a light touch for heavily constrained sgd"
    - "adam a method for stochastic optimization"
    - "deep learning face attributes in the wild"

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)