human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "input complexity and out of distribution detection with likelihood based generative models"):
  Abstract: Likelihood-based generative models are a promising resource to detect out-of-distribution (OOD) inputs which could compromise the robustness or reliability of a machine learning system. However, likelihoods derived from such models have been shown to be problematic for detecting certain types of inputs that significantly differ from training data. In this paper, we pose that this problem is due to the excessive influence that input complexity has in generative models' likelihoods. We report a set of experiments supporting this hypothesis, and use an estimate of input complexity to derive an efficient and parameter-free OOD score, which can be seen as a likelihood-ratio, akin to Bayesian model comparison. We find such score to perform comparably to, or even better than, existing OOD detection approaches under a wide range of data sets, models, model sizes, and complexity estimates.

Includes members:
  Member "uncertainty in the variational information bottleneck":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dueling decoders regularizing variational autoencoder latent spaces")
  Member "deep anomaly detection with outlier exposure":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "enhancing the reliability of out of distribution image detection in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adaptive density estimation for generative models")
  Member "data discovery and anomaly detection using atypicality theory":
    also connected through other hyperedges:
      (none)
  Member "a baseline for detecting misclassified and out of distribution examples in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "deep neural networks are easily fooled high confidence predictions for unrecognizable images":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sam the sensitivity of attribution methods to hyperparameters")
  Member "deep learning face attributes in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")

Question: Please tell me which class the source paper 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)