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: "bayesian prediction of future street scenes using synthetic likelihoods"
  Abstract: For autonomous agents to successfully operate in the real world, the ability to anticipate future scene states is a key competence. In real-world scenarios, future states become increasingly uncertain and multi-modal, particularly on long time horizons. Dropout based Bayesian inference provides a computationally tractable, theoretically well grounded approach to learn likely hypotheses/models to deal with uncertain futures and make predictions that correspond well to observations -- are well calibrated. However, it turns out that such approaches fall short to capture complex real-world scenes, even falling behind in accuracy when compared to the plain deterministic approaches. This is because the used log-likelihood estimate discourages diversity. In this work, we propose a novel Bayesian formulation for anticipating future scene states which leverages synthetic likelihoods that encourage the learning of diverse models to accurately capture the multi-modal nature of future scene states. We show that our approach achieves accurate state-of-the-art predictions and calibrated probabilities through extensive experiments for scene anticipation on Cityscapes dataset. Moreover, we show that our approach generalizes across diverse tasks such as digit generation and precipitation forecasting.

Connected through hyperedge HE-1 (induced by source paper "overcoming limitations of mixture density networks a sampling and fitting framework for multimodal future prediction"):
  with members:
    - "hierarchical long term video prediction without supervision"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "predicting future instance segmentations by forecasting convolutional features"
    - "egocentric vision based future vehicle localization for intelligent driving assistance systems"
    - "multimodal interaction aware motion prediction for autonomous street crossing"
    - "learning what you can do before doing anything"
    - "flow grounded spatial temporal video prediction from still images"
    - "action anticipation by predicting future dynamic images"

Connected through hyperedge HE-2 (induced by source paper "segmenting the future"):
  with members:
    - "predicting future instance segmentations by forecasting convolutional features"
    - "action agnostic human pose forecasting"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "context encoding for semantic segmentation"
    - "d3d distilled 3d networks for video action recognition"
    - "future semantic segmentation with convolutional lstm"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "predicting deeper into the future of semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "conditional flow variational autoencoders for structured sequence prediction"):
  with members:
    - "graph neural networks for modelling traffic participant interaction"
    - "scene induced multi modal trajectory forecasting via planning"
    - "riemannian normalizing flow on variational wasserstein autoencoder for text modeling"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "preventing posterior collapse with delta vaes"
    - "structured output learning with conditional generative flows"
    - "cyclical annealing schedule a simple approach to mitigating kl vanishing"
    - "videoflow a flow based generative model for video"

Connected through hyperedge HE-4 (induced by source paper "enhancing traffic scene predictions with generative adversarial networks"):
  with members:
    - "sequential attend infer repeat generative modelling of moving objects"
    - "learning to forecast and refine residual motion for image to video generation"
    - "reduced gate convolutional lstm using predictive coding for spatiotemporal prediction"
    - "yolov3 an incremental improvement"
    - "futuregan anticipating the future frames of video sequences using spatio temporal 3d convolutions in progressively growing gans"
    - "disentangling propagation and generation for video prediction"
    - "novel video prediction for large scale scene using optical flow"
    - "dyan a dynamical atoms based network for video prediction"

Connected through hyperedge HE-5 (induced by source paper "best of many samples distribution matching"):
  with members:
    - "joint wasserstein autoencoders for aligning multimodal embeddings"
    - "learning implicit generative models with the method of learned moments"
    - "mixture density generative adversarial networks"
    - "cramer wold autoencoder"
    - "spectral normalization for generative adversarial networks"
    - "banach wasserstein gan"
    - "large scale gan training for high fidelity natural image synthesis"
    - "dist gan an improved gan using distance constraints"

Connected through hyperedge HE-6 (induced by source paper "single level feature to feature forecasting with deformable convolutions"):
  with members:
    - "panoptic segmentation"
    - "in defense of pre trained imagenet architectures for real time semantic segmentation of road driving images"
    - "efficient ladder style densenets for semantic segmentation of large images"
    - "predicting future instance segmentations by forecasting convolutional features"
    - "recurrent flow guided semantic forecasting"
    - "deformable convnets v2 more deformable better results"
    - "predicting deeper into the future of semantic segmentation"
    - "deformable convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "uncertainty aware anticipation of activities"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "action completion a temporal model for moment detection"
    - "action anticipation by predicting future dynamic images"
    - "when will you do what anticipating temporal occurrences of activities"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "encouraging lstms to anticipate actions very early"
    - "visual forecasting by imitating dynamics in natural sequences"
    - "red reinforced encoder decoder networks for action anticipation"

Connected through hyperedge HE-8 (induced by source paper "short term motion prediction of traffic actors for autonomous driving using deep convolutional networks"):
  with members:
    - "uncertainty aware driver trajectory prediction at urban intersections"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "mastering chess and shogi by self play with a general reinforcement learning algorithm"
    - "towards full automated drive in urban environments a demonstration in gomentum station california"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "end to end tracking and semantic segmentation using recurrent 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.CV(Computer Vision and Pattern Recognition)