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: "using j k fold cross validation to reduce variance when tuning nlp models"
  Abstract: K-fold cross validation (CV) is a popular method for estimating the true performance of machine learning models, allowing model selection and parameter tuning. However, the very process of CV requires random partitioning of the data and so our performance estimates are in fact stochastic, with variability that can be substantial for natural language processing tasks. We demonstrate that these unstable estimates cannot be relied upon for effective parameter tuning. The resulting tuned parameters are highly sensitive to how our data is partitioned, meaning that we often select sub-optimal parameter choices and have serious reproducibility issues. #R##N#Instead, we propose to use the less variable J-K-fold CV, in which J independent K-fold cross validations are used to assess performance. Our main contributions are extending J-K-fold CV from performance estimation to parameter tuning and investigating how to choose J and K. We argue that variability is more important than bias for effective tuning and so advocate lower choices of K than are typically seen in the NLP literature, instead use the saved computation to increase J. To demonstrate the generality of our recommendations we investigate a wide range of case-studies: sentiment classification (both general and target-specific), part-of-speech tagging and document classification.

Connected through hyperedge HE-1 (induced by source paper "temporal and aspectual entailment"):
  with members:
    - "collecting diverse natural language inference problems for sentence representation evaluation"
    - "allennlp a deep semantic natural language processing platform"
    - "deep contextualized word representations"
    - "one representation per word does it make sense for composition"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "attention is all you need"
    - "advances in pre training distributed word representations"
    - "improving semantic composition with offset inference"

Connected through hyperedge HE-2 (induced by source paper "fiesta fast identification of state of the art models using adaptive bandit algorithms"):
  with members:
    - "why comparing single performance scores does not allow to draw conclusions about machine learning approaches"
    - "design challenges and misconceptions in neural sequence labeling"
    - "allennlp a deep semantic natural language processing platform"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "reinforcement learning for bandit neural machine translation with simulated human feedback"
    - "interactive attention networks for aspect level sentiment classification"
    - "simple bayesian algorithms for best arm identification"
    - "end to end sequence labeling via bi directional lstm cnns crf"

Connected through hyperedge HE-3 (induced by source paper "hydrocephalus verification on brain magnetic resonance images with deep convolutional neural networks and transfer learning technique"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deep convolutional neural networks for breast cancer histology image analysis"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "angiodysplasia detection and localization using deep convolutional neural networks"
    - "deep residual learning for image recognition"
    - "how transferable are features in deep neural networks"
    - "imagenet large scale visual recognition challenge"

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.CL(Computation and Language)