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: "long term forecasting using higher order tensor rnns"
  Abstract: We present Higher-Order Tensor RNN (HOT-RNN), a novel family of neural sequence architectures for multivariate forecasting in environments with nonlinear dynamics. Long-term forecasting in such systems is highly challenging, since there exist long-term temporal dependencies, higher-order correlations and sensitivity to error propagation. Our proposed recurrent architecture addresses these issues by learning the nonlinear dynamics directly using higher-order moments and higher-order state transition functions. Furthermore, we decompose the higher-order structure using the tensor-train decomposition to reduce the number of parameters while preserving the model performance. We theoretically establish the approximation guarantees and the variance bound for HOT-RNN for general sequence inputs. We also demonstrate 5% ~ 12% improvements for long-term prediction over general RNN and LSTM architectures on a range of simulated environments with nonlinear dynamics, as well on real-world time series data.

Connected through hyperedge HE-1 (induced by source paper "convolutional tensor train lstm for spatio temporal learning"):
  with members:
    - "a tensorized transformer for language modeling"
    - "efficient n dimensional convolutions via higher order factorization"
    - "robust deep networks with randomized tensor regression layers"
    - "scaling autoregressive video models"
    - "predrnn towards a resolution of the deep in time dilemma in spatiotemporal predictive learning"
    - "stochastic adversarial video prediction"
    - "stochastic video generation with a learned prior"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-2 (induced by source paper "h owan multi distorted image restoration with tensor 1x1 convolution"):
  with members:
    - "path restore learning network path selection for image restoration"
    - "crafting a toolchain for image restoration by deep reinforcement learning"
    - "efficient low rank multimodal fusion with modality specific factors"
    - "two at once enhancing learning and generalization capacities via ibn net"
    - "attention based adaptive selection of operations for image restoration in the presence of unknown combined distortions"
    - "mutan multimodal tucker fusion for visual question answering"
    - "single image super resolution via a lightweight residual convolutional neural network"
    - "attentive generative adversarial network for raindrop removal from a single image"

Connected through hyperedge HE-3 (induced by source paper "block hankel tensor arima for multiple short time series forecasting"):
  with members:
    - "manifold modeling in embedded space a perspective for interpreting deep image prior"
    - "model agnostic time series analysis via matrix estimation"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "missing slice recovery for tensors using a low rank model in embedded space"
    - "deepar probabilistic forecasting with autoregressive recurrent networks"
    - "modeling long and short term temporal patterns with deep neural networks"
    - "xgboost a scalable tree boosting system"
    - "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"

Connected through hyperedge HE-4 (induced by source paper "tensorized embedding layers for efficient model compression"):
  with members:
    - "a tensorized transformer for language modeling"
    - "pay less attention with lightweight and dynamic convolutions"
    - "transformer xl attentive language models beyond a fixed length context"
    - "albert a lite bert for self supervised learning of language representations"
    - "generalized tensor models for recurrent neural networks"
    - "word2bits quantized word vectors"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "west word encoded sequence transducers"

Connected through hyperedge HE-5 (induced by source paper "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms"):
  with members:
    - "grip graph based interaction aware trajectory prediction"
    - "robusttp end to end trajectory prediction for heterogeneous road agents in dense traffic with noisy sensor inputs"
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "self driving cars a survey"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "predicting motion of vulnerable road users using high definition maps and efficient convnets"
    - "a behavior driven approach for sampling rare event situations for autonomous vehicles"
    - "nuscenes a multimodal dataset for autonomous driving"

Connected through hyperedge HE-6 (induced by source paper "enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting"):
  with members:
    - "generating long sequences with sparse transformers"
    - "generating wikipedia by summarizing long sequences"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "deepar probabilistic forecasting with autoregressive recurrent networks"
    - "attention is all you need"
    - "modeling long and short term temporal patterns with deep neural networks"
    - "condensenet an efficient densenet using learned group convolutions"

Connected through hyperedge HE-7 (induced by source paper "bayesian tensorized neural networks with automatic rank selection"):
  with members:
    - "tucker tensor layer in fully connected neural networks"
    - "wide compression tensor ring nets"
    - "tensor regression networks"
    - "compression aware training of deep networks"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "ultimate tensorization compressing convolutional and fc layers alike"
    - "bayesian robust tensor factorization for incomplete multiway data"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"

Connected through hyperedge HE-8 (induced by source paper "data driven prediction of a multi scale lorenz 96 chaotic system using deep learning methods reservoir computing ann and rnn lstm"):
  with members:
    - "hybrid forecasting of chaotic processes using machine learning in conjunction with a knowledge based model"
    - "doing moore with less leapfrogging moore s law with inexactness for supercomputing"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "adam a method for stochastic optimization"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"
    - "speech recognition with deep recurrent neural networks"
    - "on the difficulty of training 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.LG(Machine Learning)