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: "learning sequence encoders for temporal knowledge graph completion"
  Abstract: Research on link prediction in knowledge graphs has mainly focused on static multi-relational data. In this work we consider temporal knowledge graphs where relations between entities may only hold for a time interval or a specific point in time. In line with previous work on static knowledge graphs, we propose to address this problem by learning latent entity and relation type representations. To incorporate temporal information, we utilize recurrent neural networks to learn time-aware representations of relation types which can be used in conjunction with existing latent factorization methods. The proposed approach is shown to be robust to common challenges in real-world KGs: the sparsity and heterogeneity of temporal expressions. Experiments show the benefits of our approach on four temporal KGs. The data sets are available under a permissive BSD-3 license 1.

Connected through hyperedge HE-1 (induced by source paper "temporal attribute prediction via joint modeling of multi relational structure evolution"):
  with members:
    - "evolvegcn evolving graph convolutional networks for dynamic graphs"
    - "recurrent event network for reasoning over temporal knowledge graphs"
    - "dyngem deep embedding method for dynamic graphs"
    - "representation learning on graphs methods and applications"
    - "know evolve deep reasoning in temporal knowledge graphs"
    - "convolutional 2d knowledge graph embeddings"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-2 (induced by source paper "diachronic embedding for temporal knowledge graph completion"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "relational representation learning for dynamic knowledge graphs a survey"
    - "hypernetwork knowledge graph embeddings"
    - "tucker tensor factorization for knowledge graph completion"
    - "improved knowledge graph embedding using background taxonomic information"
    - "how powerful are graph neural networks"
    - "learning dynamic embeddings from temporal interactions"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"

Connected through hyperedge HE-3 (induced by source paper "recurrent event network for reasoning over temporal knowledge graphs"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "evolvegcn evolving graph convolutional networks for dynamic graphs"
    - "predicting dynamic embedding trajectory in temporal interaction networks"
    - "relational representation learning for dynamic knowledge graphs a survey"
    - "hierarchical temporal convolutional networks for dynamic recommender systems"
    - "graph networks as learnable physics engines for inference and control"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-4 (induced by source paper "recurrent event network global structure inference over temporal knowledge graph"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "evolvegcn evolving graph convolutional networks for dynamic graphs"
    - "predicting dynamic embedding trajectory in temporal interaction networks"
    - "relational representation learning for dynamic knowledge graphs a survey"
    - "graph networks as learnable physics engines for inference and control"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "recurrent relational networks"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"

Connected through hyperedge HE-5 (induced by source paper "temporal knowledge graph embedding model based on additive time series decomposition"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "diachronic embedding for temporal knowledge graph completion"
    - "embedding entities and relations for learning and inference in knowledge bases"

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.AI(Artificial Intelligence)