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: "recurrent event network for reasoning over temporal knowledge graphs"
  Abstract: Recently, there has been a surge of interest in learning representation of graph-structured data that are dynamically evolving. However, current dynamic graph learning methods lack a principled way in modeling temporal, multi-relational, and concurrent interactions between nodes---a limitation that is especially problematic for the task of temporal knowledge graph reasoning, where the goal is to predict unseen entity relationships (i.e., events) over time. Here we present Recurrent Event Network (RE-Net)---a novel neural architecture for modeling complex event sequences---which consists of a recurrent event encoder and a neighborhood aggregator. The event encoder employs an RNN to capture (subject, relation) or (object, relation)-specific patterns from historical, multi-relational interactions between entities. The neighborhood aggregator summarizes concurrent, multi-hop entity interactions within each time stamp. An output layer is designed for predicting forthcoming events. Extensive experiments on temporal link prediction over four public TKG datasets demonstrate the effectiveness and strength of RE-Net, especially on multi-step inference over future time stamps. Code and data are published at the this https URL {\text{GitHub repository}}.

Connected through hyperedge HE-1 (induced by source paper "interpretable and fair comparison of link prediction or entity alignment methods with adjusted mean rank"):
  with members:
    - "multi view knowledge graph embedding for entity alignment"
    - "knowledge graph entity alignment with graph convolutional networks lessons learned"
    - "cross lingual knowledge graph alignment via graph matching neural network"
    - "pykg2vec a python library for knowledge graph embedding"
    - "knowledge graph alignment network with gated multi hop neighborhood aggregation"
    - "a re evaluation of knowledge graph completion methods"
    - "hypernetwork knowledge graph embeddings"
    - "learning to exploit long term relational dependencies in knowledge graphs"

Connected through hyperedge HE-2 (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"
    - "dyngem deep embedding method for dynamic graphs"
    - "learning sequence encoders for temporal knowledge graph completion"
    - "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-3 (induced by source paper "fast graph representation learning with pytorch geometric"):
  with members:
    - "graph neural networks with convolutional arma filters"
    - "simplifying graph convolutional networks"
    - "just jump dynamic neighborhood aggregation in graph neural networks"
    - "generating 3d faces using convolutional mesh autoencoders"
    - "predict then propagate graph neural networks meet personalized pagerank"
    - "pitfalls of graph neural network evaluation"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "a simple yet effective baseline for non attributed graph classification"

Connected through hyperedge HE-4 (induced by source paper "discrete and continuous deep residual learning over graphs"):
  with members:
    - "a comprehensive survey on graph neural networks"
    - "group normalization"
    - "learning a sat solver from single bit supervision"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "relational inductive biases deep learning and graph networks"
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"
    - "how powerful are graph neural networks"

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)