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: "cyclical annealing schedule a simple approach to mitigating kl vanishing"
  Abstract: Variational autoencoders (VAEs) with an auto-regressive decoder have been applied for many natural language processing (NLP) tasks. The VAE objective consists of two terms, (i) reconstruction and (ii) KL regularization, balanced by a weighting hyper-parameter \beta. One notorious training difficulty is that the KL term tends to vanish. In this paper we study scheduling schemes for \beta, and show that KL vanishing is caused by the lack of good latent codes in training the decoder at the beginning of optimization. To remedy this, we propose a cyclical annealing schedule, which repeats the process of increasing \beta multiple times. This new procedure allows the progressive learning of more meaningful latent codes, by leveraging the informative representations of previous cycles as warm re-starts. The effectiveness of cyclical annealing is validated on a broad range of NLP tasks, including language modeling, dialog response generation and unsupervised language pre-training.

Connected through hyperedge HE-1 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction"):
  with members:
    - "joint 3d tracking and forecasting with graph neural network and diversity sampling"
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "learning variations in human motion via mix and match perturbation"
    - "diverse trajectory forecasting with determinantal point processes"
    - "learning trajectory dependencies for human motion prediction"
    - "structured prediction helps 3d human motion modelling"
    - "imitation learning for human pose prediction"
    - "predicting 3d human dynamics from video"

Connected through hyperedge HE-2 (induced by source paper "joint 3d tracking and forecasting with graph neural network and diversity sampling"):
  with members:
    - "complexer yolo real time 3d object detection and tracking on semantic point clouds"
    - "grip graph based interaction aware trajectory prediction"
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "dual graph convolutional network for semantic segmentation"
    - "diverse trajectory forecasting with determinantal point processes"
    - "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms"
    - "a baseline for 3d multi object tracking"

Connected through hyperedge HE-3 (induced by source paper "effective estimation of deep generative language models"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "riemannian normalizing flow on variational wasserstein autoencoder for text modeling"
    - "preventing posterior collapse with delta vaes"
    - "neural autoregressive flows"
    - "a hierarchical latent structure for variational conversation modeling"
    - "differentiable perturb and parse semi supervised parsing with a structured variational autoencoder"
    - "spherical latent spaces for stable variational autoencoders"
    - "amr parsing as graph prediction with latent alignment"

Connected through hyperedge HE-4 (induced by source paper "dynamics aware embeddings"):
  with members:
    - "model predictive policy learning with uncertainty regularization for driving in dense traffic"
    - "model based reinforcement learning for atari"
    - "learning action representations for reinforcement learning"
    - "the natural language of actions"
    - "continual state representation learning for reinforcement learning using generative replay"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "deepmind control suite"
    - "learning latent dynamics for planning from pixels"

Connected through hyperedge HE-5 (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"
    - "videoflow a flow based generative model for video"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"

Connected through hyperedge HE-6 (induced by source paper "jointly optimizing diversity and relevance in neural response generation"):
  with members:
    - "dialog system technology challenge 7"
    - "consistent dialogue generation with self supervised feature learning"
    - "dialogwae multimodal response generation with conditional wasserstein auto encoder"
    - "the design and implementation of xiaoice an empathetic social chatbot"
    - "neural approaches to conversational ai"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "a knowledge grounded neural conversation model"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"

Connected through hyperedge HE-7 (induced by source paper "preventing posterior collapse in sequence vaes with pooling"):
  with members:
    - "implicit deep latent variable models for text generation"
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "riemannian normalizing flow on variational wasserstein autoencoder for text modeling"
    - "a hierarchical latent structure for variational conversation modeling"
    - "spherical latent spaces for stable variational autoencoders"
    - "toward controlled generation of text"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-8 (induced by source paper "unsupervised multi document opinion summarization as copycat review generation"):
  with members:
    - "seq 3 differentiable sequence to sequence to sequence autoencoder for unsupervised abstractive sentence compression"
    - "unsupervised neural single document summarization of reviews via learning latent discourse structure and its ranking"
    - "generating wikipedia by summarizing long sequences"
    - "summarizing opinions aspect extraction meets sentiment prediction and they are both weakly supervised"
    - "meansum a neural model for unsupervised multi document abstractive summarization"
    - "a deep reinforced model for abstractive summarization"
    - "abstractive text summarization using sequence to sequence rnns and beyond"
    - "using the output embedding to improve language models"

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)