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: "enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting"
  Abstract: Time series forecasting is an important problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation. In this paper, we propose to tackle such forecasting problem with Transformer. Although impressed by its performance in our preliminary study, we found its two major weaknesses: (1) locality-agnostics: the point-wise dot-product self attention in canonical Transformer architecture is insensitive to local context, which can make the model prone to anomalies in time series; (2) memory bottleneck: space complexity of canonical Transformer grows quadratically with sequence length $L$, making modeling long time series infeasible. In order to solve these two issues, we first propose convolutional self attention by producing queries and keys with causal convolution so that local context can be better incorporated into attention mechanism. Then, we propose LogSparse Transformer with only $O(L(\log L)^{2})$ memory cost, improving the time series forecasting in finer granularity under constrained memory budget. Our experiments on both synthetic data and real-world datasets show that it compares favorably to the state-of-the-art.

Connected through hyperedge HE-1 (induced by source paper "forecastnet a time variant deep feed forward neural network architecture for multi step ahead time series forecasting"):
  with members:
    - "deep transformer models for time series forecasting the influenza prevalence case"
    - "recurrent neural networks for time series forecasting current status and future directions"
    - "foundations of sequence to sequence modeling for time series"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "attention is all you need"
    - "autoregressive convolutional neural networks for asynchronous time series"
    - "wavenet a generative model for raw audio"
    - "deep learning in neural networks"

Connected through hyperedge HE-2 (induced by source paper "multi variate probabilistic time series forecasting via conditioned normalizing flows"):
  with members:
    - "high dimensional multivariate forecasting with low rank gaussian copula processes"
    - "videoflow a flow based generative model for video"
    - "n beats neural basis expansion analysis for interpretable time series forecasting"
    - "conditional recurrent flow conditional generation of longitudinal samples with applications to neuroimaging"
    - "relational inductive biases deep learning and graph networks"
    - "deepar probabilistic forecasting with autoregressive recurrent networks"
    - "attention is all you need"
    - "modeling long and short term temporal patterns with deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "blockwise self attention for long document understanding"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "latent retrieval for weakly supervised open domain question answering"
    - "adaptively sparse transformers"
    - "passage re ranking with bert"
    - "spanbert improving pre training by representing and predicting spans"
    - "matching the blanks distributional similarity for relation learning"
    - "low memory neural network training a technical report"
    - "transformer xl attentive language models beyond a fixed length context"

Connected through hyperedge HE-4 (induced by source paper "you may not need order in time series forecasting"):
  with members:
    - "parameter efficient transfer learning for nlp"
    - "the evolved transformer"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "set transformer a framework for attention based permutation invariant neural networks"
    - "self attention with relative position representations"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

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)