human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "residual attention net for superior cross domain time sequence modeling"):
  Abstract: We present a novel architecture, residual attention net (RAN), which merges a sequence architecture, universal transformer, and a computer vision architecture, residual net, with a high-way architecture for cross-domain sequence modeling. The architecture aims at addressing the long dependency issue often faced by recurrent-neural-net-based structures. This paper serves as a proof-of-concept for a new architecture, with RAN aiming at providing the model a higher level understanding of sequence patterns. To our best knowledge, we are the first to propose such an architecture. Out of the standard 85 UCR data sets, we have achieved 35 state-of-the-art results with 10 results matching current state-of-the-art results without further model fine-tuning. The results indicate that such architecture is promising in complex, long-sequence modeling and may have vast, cross-domain applications.

Includes members:
  Member "deep neural network ensembles for time series classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "detecting and interpreting myocardial infarction using fully convolutional neural networks")
  Member "insights into lstm fully convolutional networks for time series classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fully convolutional networks for structural health monitoring through multivariate time series classification")
  Member "multivariate lstm fcns for time series classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fully convolutional networks for structural health monitoring through multivariate time series classification")
  Member "ts chief a scalable and accurate forest algorithm for time series classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rocket exceptionally fast and accurate time series classification using random convolutional kernels")
  Member "star transformer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "transformer on a diet")
  Member "universal transformers":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")
  Member "linked dynamic graph cnn learning on point cloud via linking hierarchical features":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "grid gcn for fast and scalable point cloud learning")
  Member "financial series prediction using attention lstm":
    also connected through other hyperedges:
      (none)

Question: Please tell me which class the source paper 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)