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: "text understanding from scratch"
  Abstract: This article demontrates that we can apply deep learning to text understanding from character-level inputs all the way up to abstract text concepts, using temporal convolutional networks (ConvNets). We apply ConvNets to various large-scale datasets, including ontology classification, sentiment analysis, and text categorization. We show that temporal ConvNets can achieve astonishing performance without the knowledge of words, phrases, sentences and any other syntactic or semantic structures with regards to a human language. Evidence shows that our models can work for both English and Chinese.

Connected through hyperedge HE-1 (induced by source paper "filter grafting for deep neural networks"):
  with members:
    - "omni scale feature learning for person re identification"
    - "importance estimation for neural network pruning"
    - "exploring linear relationship in feature map subspace for convnets compression"
    - "filter pruning via geometric median for deep convolutional neural networks acceleration"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "network transplanting"
    - "scsp spectral clustering filter pruning with soft self adaption manners"
    - "network compression using correlation analysis of layer responses"

Connected through hyperedge HE-2 (induced by source paper "deep weakly supervised anomaly detection"):
  with members:
    - "deep anomaly detection with deviation networks"
    - "deep semi supervised anomaly detection"
    - "efficient training for positive unlabeled learning"
    - "adversarially learned anomaly detection"
    - "learning representations of ultrahigh dimensional data for random distance based outlier detection"
    - "one class adversarial nets for fraud detection"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-3 (induced by source paper "iot network behavioral fingerprint inference with limited network trace for cyber investigation a meta learning approach"):
  with members:
    - "one class convolutional neural network"
    - "prototypical networks for few shot learning"
    - "iot sentinel demo automated device type identification for security enforcement in iot"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"

Connected through hyperedge HE-4 (induced by source paper "selection via proxy efficient data selection for deep learning"):
  with members:
    - "batchbald efficient and diverse batch acquisition for deep bayesian active learning"
    - "weight agnostic neural networks"
    - "how transferable are the datasets collected by active learners"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "on the relationship between data efficiency and error for uncertainty sampling"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "active learning for interactive neural machine translation of data streams"
    - "an empirical study of example forgetting during deep neural network learning"

Connected through hyperedge HE-5 (induced by source paper "protoattend attention based prototypical learning"):
  with members:
    - "on the robustness of deep k nearest neighbors"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "representer point selection for explaining deep neural networks"
    - "this looks like that deep learning for interpretable image recognition"
    - "deep anomaly detection with outlier exposure"
    - "catboost gradient boosting with categorical features support"
    - "incremental few shot learning with attention attractor networks"
    - "attention based ensemble for deep metric learning"

Connected through hyperedge HE-6 (induced by source paper "amsi based detection of malicious powershell code using contextual embeddings"):
  with members:
    - "attack2vec leveraging temporal word embeddings to understand the evolution of cyberattacks"
    - "ast based deep learning for detecting malicious powershell"
    - "neural classification of malicious scripts a study with javascript and vbscript"
    - "deep contextualized word representations"
    - "detecting malicious powershell commands using deep neural networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "expose a character level convolutional neural network with embeddings for detecting malicious urls file paths and registry keys"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "core automating review recommendation for code changes"):
  with members:
    - "an empirical assessment of security risks of global android banking apps"
    - "pythia ai assisted code completion system"
    - "an empirical study towards characterizing deep learning development and deployment across different frameworks and platforms"
    - "storydroid automated generation of storyboard for android apps"
    - "ausera large scale automated security risk assessment of global mobile banking apps"
    - "efficiently manifesting asynchronous programming errors in android apps"
    - "code completion with neural attention and pointer networks"
    - "deep contextualized word representations"

Connected through hyperedge HE-8 (induced by source paper "educe explaining model decisions through unsupervised concepts extraction"):
  with members:
    - "xoc explainable observer classifier for explainable binary decisions"
    - "interpretable neural predictions with differentiable binary variables"
    - "counterfactual visual explanations"
    - "learning word vectors for 157 languages"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a causal framework for explaining the predictions of black box sequence to sequence models"
    - "axiomatic attribution for deep networks"
    - "rationalizing neural predictions"

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)