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: "anomaly machine component detection by deep generative model with unregularized score"
  Abstract: One of the most common needs in manufacturing plants is rejecting products not coincident with the standards as anomalies. Accurate and automatic anomaly detection improves product reliability and reduces inspection cost. Probabilistic models have been employed to detect test samples with lower likelihoods as anomalies in unsupervised manner. Recently, a probabilistic model called deep generative model (DGM) has been proposed for end-to-end modeling of natural images and already achieved a certain success. However, anomaly detection of machine components with complicated structures is still challenging because they produce a wide variety of normal image patches with low likelihoods. For overcoming this difficulty, we propose unregularized score for the DGM. As its name implies, the unregularized score is the anomaly score of the DGM without the regularization terms. The unregularized score is robust to the inherent complexity of a sample and has a smaller risk of rejecting a sample appearing less frequently but being coincident with the standards.

Connected through hyperedge HE-1 (induced by source paper "iterative energy based projection on a normal data manifold for anomaly localization"):
  with members:
    - "unsupervised anomaly localization using variational auto encoders"
    - "improving unsupervised defect segmentation by applying structural similarity to autoencoders"
    - "diagnosing and enhancing vae models"
    - "classification accuracy score for conditional generative models"
    - "improving generalization and stability of generative adversarial networks"
    - "deep structured energy based image inpainting"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "nips 2016 tutorial generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "exploring uncertainty measures for image caption embedding and retrieval task"):
  with members:
    - "a closer look at few shot classification"
    - "exploring uncertainty in conditional multi modal retrieval systems"
    - "quantifying uncertainties in natural language processing tasks"
    - "recipeqa a challenge dataset for multimodal comprehension of cooking recipes"
    - "object hallucination in image captioning"
    - "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "dual path convolutional image text embedding with instance loss"

Connected through hyperedge HE-3 (induced by source paper "deep generative model using unregularized score for anomaly detection with heterogeneous complexity"):
  with members:
    - "deep structured energy based models for anomaly detection"
    - "importance weighted autoencoders"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "semi supervised learning with deep generative models"
    - "deep generative stochastic networks trainable by backprop"

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)