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: "binary classifier calibration using an ensemble of near isotonic regression models"
  Abstract: Learning accurate probabilistic models from data is crucial in many practical tasks in data mining. In this paper we present a new non-parametric calibration method called \textit{ensemble of near isotonic regression} (ENIR). The method can be considered as an extension of BBQ, a recently proposed calibration method, as well as the commonly used calibration method based on isotonic regression. ENIR is designed to address the key limitation of isotonic regression which is the monotonicity assumption of the predictions. Similar to BBQ, the method post-processes the output of a binary classifier to obtain calibrated probabilities. Thus it can be combined with many existing classification models. We demonstrate the performance of ENIR on synthetic and real datasets for the commonly used binary classification models. Experimental results show that the method outperforms several common binary classifier calibration methods. In particular on the real data, ENIR commonly performs statistically significantly better than the other methods, and never worse. It is able to improve the calibration power of classifiers, while retaining their discrimination power. The method is also computationally tractable for large scale datasets, as it is $O(N \log N)$ time, where $N$ is the number of samples.

Connected through hyperedge HE-1 (induced by source paper "temporal probability calibration"):
  with members:
    - "calibration of encoder decoder models for neural machine translation"
    - "when does label smoothing help"
    - "huggingface s transformers state of the art natural language processing"
    - "measuring calibration in deep learning"
    - "pytorch an imperative style high performance deep learning library"
    - "probability calibration trees"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "on calibration of modern neural networks"

Connected through hyperedge HE-2 (induced by source paper "calibrating deep neural networks using focal loss"):
  with members:
    - "when does label smoothing help"
    - "on calibration of modern neural networks"
    - "focal loss for dense object detection"
    - "attention is all you need"
    - "regularizing neural networks by penalizing confident output distributions"
    - "wide residual networks"
    - "deep residual learning for image recognition"
    - "improved semantic representations from tree structured long short term memory networks"

Connected through hyperedge HE-3 (induced by source paper "better multi class probability estimates for small data sets"):
  with members:
    - "better classifier calibration for small data sets"

Connected through hyperedge HE-4 (induced by source paper "beyond temperature scaling obtaining well calibrated multiclass probabilities with dirichlet calibration"):
  with members:
    - "verified uncertainty calibration"
    - "evaluating model calibration in classification"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "dirichlet based gaussian processes for large scale calibrated classification"
    - "accurate uncertainties for deep learning using calibrated regression"
    - "on calibration of modern neural networks"
    - "deep networks with stochastic depth"
    - "densely connected convolutional networks"

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)