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 "recurrent event network global structure inference over temporal knowledge graph"):
  Abstract: Modeling dynamically-evolving, multi-relational graph data has received a surge of interests with the rapid growth of heterogeneous event data. However, predicting future events on such data requires global structure inference over time and the ability to integrate temporal and structural information, which are not yet well understood. We present Recurrent Event Network (RE-Net), a novel autoregressive architecture for modeling temporal sequences of multi-relational graphs (e.g., temporal knowledge graph), which can perform sequential, global structure inference over future time stamps to predict new events. RE-Net employs a recurrent event encoder to model the temporally conditioned joint probability distribution for the event sequences, and equips the event encoder with a neighborhood aggregator for modeling the concurrent events within a time window associated with each entity. We apply teacher forcing for model training over historical data, and infer graph sequences over future time stamps by sampling from the learned joint distribution in a sequential manner. We evaluate the proposed method via temporal link prediction on five public datasets. Extensive experiments demonstrate the strength of RE-Net, especially on multi-step inference over future time stamps. Code and data can be found at this https URL .

Includes members:
  Member "rotate knowledge graph embedding by relational rotation in complex space":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a benchmarking study of embedding based entity alignment for knowledge graphs")
  Member "evolvegcn evolving graph convolutional networks for dynamic graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "temporal attribute prediction via joint modeling of multi relational structure evolution")
  Member "predicting dynamic embedding trajectory in temporal interaction networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning multivariate hawkes processes at scale")
  Member "relational representation learning for dynamic knowledge graphs a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "exem expert embedding using dominating set theory with deep learning approaches")
  Member "graph networks as learnable physics engines for inference and control":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "benchmarking graph neural networks")
  Member "graphrnn generating realistic graphs with deep auto regressive models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hierarchical generation of molecular graphs using structural motifs")
  Member "learning sequence encoders for temporal knowledge graph completion":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "temporal attribute prediction via joint modeling of multi relational structure evolution")
  Member "recurrent relational networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "disentangling physical dynamics from unknown factors for unsupervised video prediction")

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)