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: "enhancing the robustness of prior network in out of distribution detection"
  Abstract: With the recent surge of interests in deep neural networks, more real-world applications start to adopt it in practice. However, deep neural networks are known to have limited control over its prediction under unseen images. Such weakness can potentially threaten society and cause annoying consequences in real-world scenarios. In order to resolve such issue, a popular task called out-of-distribution detection was proposed, which aims at separating out-of-distribution images from in-distribution images. In this paper, we propose a perturbed prior network architecture, which can efficiently separate model-level uncertainty from data-level uncertainty via prior entropy. To further enhance the robustness of proposed entropy-based uncertainty measure, we propose a concentration perturbation algorithm, which adaptively adds noise to concentration parameters so that the in- and out-of-distribution images are better separable. Our method can directly rely on the pre-trained deep neural network without re-training it, and also requires no knowledge about the network architecture and out-of-distribution examples. Such simplicity makes our method more suitable for real-world AI applications. Through comprehensive experiments, our methods demonstrate its superiority by achieving state-of-the-art results on many datasets.

Connected through hyperedge HE-1 (induced by source paper "being bayesian about categorical probability"):
  with members:
    - "when does label smoothing help"
    - "momentum contrast for unsupervised visual representation learning"
    - "functional variational bayesian neural networks"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "evidential deep learning to quantify classification uncertainty"
    - "a variational dirichlet framework for out of distribution detection"
    - "realistic evaluation of deep semi supervised learning algorithms"

Connected through hyperedge HE-2 (induced by source paper "using pre training can improve model robustness and uncertainty"):
  with members:
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "logit pairing methods can fool gradient based attacks"
    - "feature denoising for improving adversarial robustness"
    - "dimensionality driven learning with noisy labels"
    - "deep anomaly detection with outlier exposure"
    - "co teaching robust training of deep neural networks with extremely noisy labels"
    - "imbalanced deep learning by minority class incremental rectification"
    - "generalisation in humans and deep neural networks"

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)