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: "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
  Abstract: PixelCNNs are a recently proposed class of powerful generative models with tractable likelihood. Here we discuss our implementation of PixelCNNs which we make available at this https URL Our implementation contains a number of modifications to the original model that both simplify its structure and improve its performance. 1) We use a discretized logistic mixture likelihood on the pixels, rather than a 256-way softmax, which we find to speed up training. 2) We condition on whole pixels, rather than R/G/B sub-pixels, simplifying the model structure. 3) We use downsampling to efficiently capture structure at multiple resolutions. 4) We introduce additional short-cut connections to further speed up optimization. 5) We regularize the model using dropout. Finally, we present state-of-the-art log likelihood results on CIFAR-10 to demonstrate the usefulness of these modifications.

Connected through hyperedge HE-1 (induced by source paper "adaptive density estimation for generative models"):
  with members:
    - "improving generalization and stability of generative adversarial networks"
    - "cgans with projection discriminator"
    - "how good is my gan"
    - "spectral normalization for generative adversarial networks"
    - "improving gans using optimal transport"
    - "large scale gan training for high fidelity natural image synthesis"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"
    - "fast generation for convolutional autoregressive models"

Connected through hyperedge HE-2 (induced by source paper "towards gan benchmarks which require generalization"):
  with members:
    - "quantitatively evaluating gans with divergences proposed for training"
    - "a call for clarity in reporting bleu scores"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "deep reinforcement learning that matters"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "comparison of maximum likelihood and gan based training of real nvps"

Connected through hyperedge HE-3 (induced by source paper "polygen an autoregressive generative model of 3d meshes"):
  with members:
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "the curious case of neural text degeneration"
    - "stabilizing transformers for reinforcement learning"
    - "generating long sequences with sparse transformers"
    - "image transformer"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"

Connected through hyperedge HE-4 (induced by source paper "source separation with deep generative priors"):
  with members:
    - "generative modeling by estimating gradients of the data distribution"
    - "music source separation in the waveform domain"
    - "end to end music source separation is it possible in the waveform domain"
    - "single image reflection separation with perceptual losses"
    - "neural separation of observed and unobserved distributions"
    - "wave u net a multi scale neural network for end to end audio source separation"
    - "dynamic routing between capsules"
    - "adversarial semi supervised audio source separation applied to singing voice extraction"

Connected through hyperedge HE-5 (induced by source paper "likelihood regret an out of distribution detection score for variational auto encoder"):
  with members:
    - "exact information bottleneck with invertible neural networks getting the best of discriminative and generative modeling"
    - "time series anomaly detection with variational autoencoders"
    - "your classifier is secretly an energy based model and you should treat it like one"
    - "input complexity and out of distribution detection with likelihood based generative models"
    - "unsupervised out of distribution detection with batch normalization"
    - "unsupervised anomaly detection via variational auto encoder for seasonal kpis in web applications"
    - "deep anomaly detection with outlier exposure"
    - "anomaly detection with generative adversarial networks for multivariate time series"

Connected through hyperedge HE-6 (induced by source paper "generalized odin detecting out of distribution image without learning from out of distribution data"):
  with members:
    - "hyperparameter free out of distribution detection using softmax of scaled cosine similarity"
    - "moment matching for multi source domain adaptation"
    - "reducing network agnostophobia"
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "deep anomaly detection with outlier exposure"
    - "metric learning for novelty and anomaly detection"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "on calibration of modern neural networks"

Connected through hyperedge HE-7 (induced by source paper "closing the dequantization gap pixelcnn as a single layer flow"):
  with members:
    - "learning discrete distributions by dequantization"
    - "integer discrete flows and lossless compression"
    - "sum of squares polynomial flow"
    - "discrete flows invertible generative models of discrete data"
    - "generating long sequences with sparse transformers"
    - "image transformer"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"
    - "ffjord free form continuous dynamics for scalable reversible generative models"

Connected through hyperedge HE-8 (induced by source paper "understanding the limitations of conditional generative models"):
  with members:
    - "hybrid models with deep and invertible features"
    - "semi supervised learning with normalizing flows"
    - "are generative classifiers more robust to adversarial attacks"
    - "towards the first adversarially robust neural network model on mnist"
    - "darccc detecting adversaries by reconstruction from class conditional capsules"
    - "excessive invariance causes adversarial vulnerability"
    - "i revnet deep invertible networks"
    - "threat of adversarial attacks on deep learning in computer vision a survey"

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)