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: "a study in rashomon curves and volumes a new perspective on generalization and model simplicity in machine learning"
  Abstract: The Rashomon effect occurs when many different explanations exist for the same phenomenon. In machine learning, Leo Breiman used this term to describe problems where many accurate-but-different models exist to describe the same data. In this work, we study how the Rashomon effect can be useful for understanding the relationship between training and test performance, and the possibility that simple-yet-accurate models exist for many problems. We introduce the Rashomon set as the set of almost-equally-accurate models for a given problem, and study its properties and the types of models it could contain. We present the Rashomon ratio as a new measure related to simplicity of model classes, which is the ratio of the volume of the set of accurate models to the volume of the hypothesis space; the Rashomon ratio is different from standard complexity measures from statistical learning theory. For a hierarchy of hypothesis spaces, the Rashomon ratio can help modelers to navigate the trade-off between simplicity and accuracy in a surprising way. In particular, we find empirically that a plot of empirical risk vs. Rashomon ratio forms a characteristic $\Gamma$-shaped Rashomon curve, whose elbow seems to be a reliable model selection criterion. When the Rashomon set is large, models that are accurate - but that also have various other useful properties - can often be obtained. These models might obey various constraints such as interpretability, fairness, monotonicity, and computational benefits.

Connected through hyperedge HE-1 (induced by source paper "predictive multiplicity in classification"):
  with members:
    - "fairwashing the risk of rationalization"
    - "problem formulation and fairness"
    - "aequitas a bias and fairness audit toolkit"
    - "it s reducing a human being to a percentage perceptions of justice in algorithmic decisions"
    - "classification with fairness constraints a meta algorithm with provable guarantees"
    - "how to use heuristics for differential privacy"
    - "the dataset nutrition label a framework to drive higher data quality standards"
    - "interventions over predictions reframing the ethical debate for actuarial risk assessment"

Connected through hyperedge HE-2 (induced by source paper "detecting extrapolation with local ensembles"):
  with members:
    - "deep weighted averaging classifiers"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "on calibration of modern neural networks"
    - "empirical analysis of the hessian of over parametrized neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "online learning for sparse pca in high dimensions exact dynamics and phase transitions"

Connected through hyperedge HE-3 (induced by source paper "ensembles of locally independent prediction models"):
  with members:
    - "improving adversarial robustness via promoting ensemble diversity"
    - "how complex is your classification problem a survey on measuring classification complexity"
    - "learning qualitatively diverse and interpretable rules for classification"
    - "snapshot ensembles train 1 get m for free"
    - "learning independent causal mechanisms"
    - "right for the right reasons training differentiable models by constraining their explanations"
    - "why should i trust you explaining the predictions of any classifier"
    - "intriguing properties of neural 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)