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: "relational representation learning for dynamic knowledge graphs a survey"
  Abstract: Graphs arise naturally in many real-world applications including social networks, recommender systems, ontologies, biology, and computational finance. Traditionally, machine learning models for graphs have been mostly designed for static graphs. However, many applications involve evolving graphs. This introduces important challenges for learning and inference since nodes, attributes, and edges change over time. In this survey, we review the recent advances in representation learning for dynamic graphs, including dynamic knowledge graphs. We describe existing models from an encoder-decoder perspective, categorize these encoders and decoders based on the techniques they employ, and analyze the approaches in each category. We also review several prominent applications and widely used datasets, and highlight directions for future research.

Connected through hyperedge HE-1 (induced by source paper "exem expert embedding using dominating set theory with deep learning approaches"):
  with members:
    - "dyngraph2vec capturing network dynamics using dynamic graph representation learning"
    - "community aware random walk for network embedding"
    - "dynnode2vec scalable dynamic network embedding"
    - "graph embedding techniques applications and performance a survey"
    - "dynamic graph representation learning via self attention networks"
    - "attributed social network embedding"
    - "network representation learning a survey"
    - "a survey on network embedding"

Connected through hyperedge HE-2 (induced by source paper "time2vec learning a vector representation of time"):
  with members:
    - "retainvis visual analytics with interpretable and interactive recurrent neural networks on electronic medical records"
    - "learning temporal point processes via reinforcement learning"
    - "neural decomposition of time series data for effective generalization"
    - "can recurrent neural networks warp time"
    - "deep reinforcement learning of marked temporal point processes"
    - "streaming graph neural networks"
    - "neural ordinary differential equations"
    - "learning dynamic embeddings from temporal interactions"

Connected through hyperedge HE-3 (induced by source paper "diachronic embedding for temporal knowledge graph completion"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "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"
    - "learning sequence encoders for temporal knowledge graph completion"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"

Connected through hyperedge HE-4 (induced by source paper "drum end to end differentiable rule mining on knowledge graphs"):
  with members:
    - "variational graph recurrent neural networks"
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "semi implicit graph variational auto encoders"
    - "tucker tensor factorization for knowledge graph completion"
    - "multi hop knowledge graph reasoning with reward shaping"
    - "graph embedding techniques applications and performance a survey"
    - "towards neural theorem proving at scale"
    - "learning explanatory rules from noisy data"

Connected through hyperedge HE-5 (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"
    - "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"
    - "learning sequence encoders for temporal knowledge graph completion"

Connected through hyperedge HE-6 (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"
    - "graph networks as learnable physics engines for inference and control"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "learning sequence encoders for temporal knowledge graph completion"
    - "recurrent relational networks"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"

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)