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 "modeling combinatorial evolution in time series prediction"):
  Abstract: Time series modeling aims to capture the intrinsic factors underpinning observed data and its evolution. However, most existing studies ignore the evolutionary relations among these factors, which are what cause the combinatorial evolution of a given time series. In this paper, we propose to represent time-varying relations among intrinsic factors of time series data by means of an evolutionary state graph structure. Accordingly, we propose the Evolutionary Graph Recurrent Networks (EGRN) to learn representations of these factors, along with the given time series, using a graph neural network framework. The learned representations can then be applied to time series classification tasks. From our experiment results, based on six real-world datasets, it can be seen that our approach clearly outperforms ten state-of-the-art baseline methods (e.g. +5% in terms of accuracy, and +15% in terms of F1 on average). In addition, we demonstrate that due to the graph structure's improved interpretability, our method is also able to explain the logical causes of the predicted events.

Includes members:
  Member "relational neural expectation maximization unsupervised discovery of objects and their interactions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "unsupervised anomaly detection via variational auto encoder for seasonal kpis in web applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "likelihood regret an out of distribution detection score for variational auto encoder")
  Member "graph networks as learnable physics engines for inference and control":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "benchmarking graph neural networks")
  Member "multilevel wavelet decomposition network for interpretable time series analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep learning for time series classification a review")
  Member "high order graph convolutional recurrent neural network a deep learning framework for network scale traffic learning and forecasting":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "physical virtual collaboration graph network for station level metro ridership prediction")
  Member "relational inductive bias for physical construction in humans and machines":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "neural consciousness flow")
  Member "action schema networks generalised policies with deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generalized neural policies for relational mdps")
  Member "toeplitz inverse covariance based clustering of multivariate time series data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "indirect adversarial attacks via poisoning neighbors for graph convolutional networks")

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)