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: "deep learning for classical japanese literature"
  Abstract: Much of machine learning research focuses on producing models which perform well on benchmark tasks, in turn improving our understanding of the challenges associated with those tasks. From the perspective of ML researchers, the content of the task itself is largely irrelevant, and thus there have increasingly been calls for benchmark tasks to more heavily focus on problems which are of social or cultural relevance. In this work, we introduce Kuzushiji-MNIST, a dataset which focuses on Kuzushiji (cursive Japanese), as well as two larger, more challenging datasets, Kuzushiji-49 and Kuzushiji-Kanji. Through these datasets, we wish to engage the machine learning community into the world of classical Japanese literature. Dataset available at this https URL

Connected through hyperedge HE-1 (induced by source paper "improve sgd training via aligning min batches"):
  with members:
    - "convolutional networks with dense connectivity"
    - "cold case the lost mnist digits"
    - "pytorch an imperative style high performance deep learning library"
    - "dropblock a regularization method for convolutional networks"
    - "escaping saddles with stochastic gradients"
    - "exploring generalization in deep learning"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "mmd gan towards deeper understanding of moment matching network"

Connected through hyperedge HE-2 (induced by source paper "lifelong learning with searchable extension units"):
  with members:
    - "learn to grow a continual structure learning framework for overcoming catastrophic forgetting"
    - "continual lifelong learning with neural networks a review"
    - "multinomial distribution learning for effective neural architecture search"
    - "incremental classifier learning with generative adversarial networks"
    - "reinforced continual learning"
    - "darts differentiable architecture search"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "memory aware synapses learning what not to forget"

Connected through hyperedge HE-3 (induced by source paper "kaokore a pre modern japanese art facial expression dataset"):
  with members:
    - "tailorgan making user defined fashion designs"
    - "learning to paint with model based deep reinforcement learning"
    - "neural painters a learned differentiable constraint for generating brushstroke paintings"
    - "kuronet pre modern japanese kuzushiji character recognition with deep learning"
    - "pytorch an imperative style high performance deep learning library"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "full body high resolution anime generation with progressive structure conditional generative adversarial networks"
    - "synthesizing programs for images using reinforced adversarial learning"

Connected through hyperedge HE-4 (induced by source paper "do we need zero training loss after achieving zero training error"):
  with members:
    - "sgd on neural networks learns functions of increasing complexity"
    - "augmenting data with mixup for sentence classification an empirical study"
    - "deep double descent where bigger models and more data hurt"
    - "pumpout a meta approach to robust deep learning with noisy labels"
    - "mixmatch a holistic approach to semi supervised learning"
    - "pumpout a meta approach for robustly training deep neural networks with noisy labels"
    - "positive unlabeled learning with non negative risk estimator"
    - "decoupled weight decay regularization"

Connected through hyperedge HE-5 (induced by source paper "sampling prediction matching examples in neural networks a probabilistic programming approach"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "scenic a language for scenario specification and scene generation"
    - "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"
    - "on evaluating adversarial robustness"
    - "pyro deep universal probabilistic programming"
    - "certified adversarial robustness with additive noise"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "on calibration of modern neural networks"

Connected through hyperedge HE-6 (induced by source paper "geometric dataset distances via optimal transport"):
  with members:
    - "adaptive gradient based meta learning methods"
    - "dynamics and reachability of learning tasks"
    - "hierarchical optimal transport for document representation"
    - "transferability and hardness of supervised classification tasks"
    - "task2vec task embedding for meta learning"
    - "learning embeddings into entropic wasserstein spaces"
    - "gromov wasserstein alignment of word embedding spaces"
    - "deep contextualized word representations"

Connected through hyperedge HE-7 (induced by source paper "fast predictive uncertainty for classification with bayesian deep networks"):
  with members:
    - "backpack packing more into backprop"
    - "evaluating uncertainty quantification in end to end autonomous driving control"
    - "evidential deep learning to quantify classification uncertainty"
    - "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem"
    - "a baseline for detecting misclassified and out of distribution examples in neural networks"
    - "deep residual learning for image recognition"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "deep neural networks are easily fooled high confidence predictions for unrecognizable images"

Connected through hyperedge HE-8 (induced by source paper "on the learning property of logistic and softmax losses for deep neural networks"):
  with members:
    - "class balanced loss based on effective number of samples"
    - "exploring the limits of weakly supervised pretraining"
    - "single label multi class image classification by deep logistic regression"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

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.CV(Computer Vision and Pattern Recognition)