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: "detecting semantic anomalies"
  Abstract: We critically appraise the recent interest in out-of-distribution (OOD) detection and question the practical relevance of existing benchmarks. While the currently prevalent trend is to consider different datasets as OOD, we argue that out-distributions of practical interest are ones where the distinction is semantic in nature for a specified context, and that evaluative tasks should reflect this more closely. Assuming a context of object recognition, we recommend a set of benchmarks, motivated by practical applications. We make progress on these benchmarks by exploring a multi-task learning based approach, showing that auxiliary objectives for improved semantic awareness result in improved semantic anomaly detection, with accompanying generalization benefits.

Connected through hyperedge HE-1 (induced by source paper "natural adversarial examples"):
  with members:
    - "hellaswag can a machine really finish your sentence"
    - "towards neural networks that provably know when they don t know"
    - "verified uncertainty calibration"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs"
    - "on evaluating adversarial robustness"

Connected through hyperedge HE-2 (induced by source paper "bayesian variational autoencoders for unsupervised out of distribution detection"):
  with members:
    - "a cold approach to generating optimal samples"
    - "conditioning by adaptive sampling for robust design"
    - "all smiles variational autoencoder"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "icebreaker element wise active information acquisition with bayesian deep latent gaussian model"
    - "efficient gan based anomaly detection"
    - "uncertainty in the variational information bottleneck"
    - "ganomaly semi supervised anomaly detection via adversarial training"

Connected through hyperedge HE-3 (induced by source paper "high and low level image component decomposition using vaes for improved reconstruction and anomaly detection"):
  with members:
    - "diagnosing and enhancing vae models"
    - "generating diverse high fidelity images with vq vae 2"
    - "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos"
    - "deep generative models in the real world an open challenge from medical imaging"
    - "deep autoencoding models for unsupervised anomaly segmentation in brain mr images"
    - "variational autoencoders pursue pca directions by accident"
    - "learning hierarchical features from generative models"
    - "u net convolutional networks for biomedical image segmentation"

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)