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: "a variational dirichlet framework for out of distribution detection"
  Abstract: With the recently rapid development in deep learning, deep neural networks have been widely adopted in many real-life applications. However, deep neural networks are also known to have very little control over its uncertainty for unseen examples, which potentially causes very harmful and annoying consequences in practical scenarios. In this paper, we are particularly interested in designing a higher-order uncertainty metric for deep neural networks and investigate its effectiveness under the out-of-distribution detection task proposed by~\cite{hendrycks2016baseline}. Our method first assumes there exists an underlying higher-order distribution $\mathbb{P}(z)$, which controls label-wise categorical distribution $\mathbb{P}(y)$ over classes on the K-dimension simplex, and then approximate such higher-order distribution via parameterized posterior function $p_{\theta}(z|x)$ under variational inference framework, finally we use the entropy of learned posterior distribution $p_{\theta}(z|x)$ as uncertainty measure to detect out-of-distribution examples. Further, we propose an auxiliary objective function to discriminate against synthesized adversarial examples to further increase the robustness of the proposed uncertainty measure. Through comprehensive experiments on various datasets, our proposed framework is demonstrated to consistently outperform competing algorithms.

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"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "enhancing the robustness of prior network in out of distribution detection"

Connected through hyperedge HE-2 (induced by source paper "detecting out of distribution examples with in distribution examples and gram matrices"):
  with members:
    - "unsupervised out of distribution detection by maximum classifier discrepancy"
    - "using self supervised learning can improve model robustness and uncertainty"
    - "input complexity and out of distribution detection with likelihood based generative models"
    - "out of distribution detection using neural rendering generative models"
    - "bayesian variational autoencoders for unsupervised out of distribution detection"
    - "unsupervised out of distribution detection with batch normalization"
    - "detecting out of distribution inputs in deep neural networks using an early layer output"
    - "out of distribution detection in classifiers via generation"

Connected through hyperedge HE-3 (induced by source paper "dirichlet uncertainty wrappers for actionable algorithm accuracy accountability and auditability"):
  with members:
    - "selectivenet a deep neural network with an integrated reject option"
    - "lightweight probabilistic deep networks"
    - "selective classification for deep neural networks"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "concrete problems in ai safety"

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)