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: "on the consistency of ordinal regression methods"
  Abstract: Many of the ordinal regression models that have been proposed in the literature can be seen as methods that minimize a convex surrogate of the zero-one, absolute, or squared loss functions. A key property that allows to study the statistical implications of such approximations is that of Fisher consistency. Fisher consistency is a desirable property for surrogate loss functions and implies that in the population setting, i.e., if the probability distribution that generates the data were available, then optimization of the surrogate would yield the best possible model. In this paper we will characterize the Fisher consistency of a rich family of surrogate loss functions used in the context of ordinal regression, including support vector ordinal regression, ORBoosting and least absolute deviation. We will see that, for a family of surrogate loss functions that subsumes support vector ordinal regression and ORBoosting, consistency can be fully characterized by the derivative of a real-valued function at zero, as happens for convex margin-based surrogates in binary classification. We also derive excess risk bounds for a surrogate of the absolute error that generalize existing risk bounds for binary classification. Finally, our analysis suggests a novel surrogate of the squared error loss. We compare this novel surrogate with competing approaches on 9 different datasets. Our method shows to be highly competitive in practice, outperforming the least squares loss on 7 out of 9 datasets.

Connected through hyperedge HE-1 (induced by source paper "feature learning to automatically assess radiographic knee osteoarthritis severity"):
  with members:
    - "automatic liver and tumor segmentation of ct and mri volumes using cascaded fully convolutional neural networks"
    - "cnn based segmentation of medical imaging data"
    - "brain tumor segmentation with deep neural networks"
    - "automatic knee osteoarthritis diagnosis from plain radiographs a deep learning based approach"
    - "automatic detection of knee joints and quantification of knee osteoarthritis severity using convolutional neural networks"
    - "a novel method for automatic localization of joint area on knee plain radiographs"
    - "a survey on deep learning in medical image analysis"
    - "deep convolutional networks for automated detection of posterior element fractures on spine ct"

Connected through hyperedge HE-2 (induced by source paper "semi supervised ordinal regression based on empirical risk minimization"):
  with members:
    - "chainer a deep learning framework for accelerating the research cycle"
    - "classification from pairwise similarity and unlabeled data"
    - "positive unlabeled learning with non negative risk estimator"
    - "loss factorization weakly supervised learning and label noise robustness"
    - "semi supervised classification based on classification from positive and unlabeled data"
    - "theoretical comparisons of positive unlabeled learning against positive negative learning"
    - "transductive ordinal regression"
    - "agnostic learning of monomials by halfspaces is hard"

Connected through hyperedge HE-3 (induced by source paper "a general theory for structured prediction with smooth convex surrogates"):
  with members:
    - "sharp analysis of learning with discrete losses"
    - "on structured prediction theory with calibrated convex surrogate losses"
    - "exponential convergence of testing error for stochastic gradient methods"
    - "a consistent regularization approach for structured prediction"
    - "consistent algorithms for multiclass classification with a reject option"
    - "exponential family graph matching and ranking"

Connected through hyperedge HE-4 (induced by source paper "feature relevance determination for ordinal regression in the context of feature redundancies and privileged information"):
  with members:
    - "delayed impact of fair machine learning"
    - "solving linear programs in the current matrix multiplication time"
    - "equality of opportunity in supervised learning"
    - "resampling methods for parameter free and robust feature selection with mutual information"

Connected through hyperedge HE-5 (induced by source paper "ap perf incorporating generic performance metrics in differentiable learning"):
  with members:
    - "training deep neural networks via direct loss minimization"
    - "online and stochastic gradient methods for non decomposable loss functions"
    - "julia a fresh approach to numerical computing"

Connected through hyperedge HE-6 (induced by source paper "quantifying learning guarantees for convex but inconsistent surrogates"):
  with members:
    - "on structured prediction theory with calibrated convex surrogate losses"
    - "exponential convergence of testing error for stochastic gradient methods"
    - "a consistent regularization approach for structured prediction"
    - "convex calibration dimension for multiclass loss matrices"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "minimizing the misclassification error rate using a surrogate convex loss"

Connected through hyperedge HE-7 (induced by source paper "sharp analysis of learning with discrete losses"):
  with members:
    - "a consistent regularization approach for structured prediction"
    - "convex calibration dimension for multiclass loss matrices"
    - "a theoretical analysis of ndcg type ranking measures"

Connected through hyperedge HE-8 (induced by source paper "using posters to recommend anime and mangas in a cold start scenario"):
  with members:
    - "specializing joint representations for the task of product recommendation"
    - "parvecmf a paragraph vector based matrix factorization recommender system"
    - "combating the cold start user problem in model based collaborative filtering"
    - "metadata embeddings for user and item cold start recommendations"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "feature weighted linear stacking"

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)