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: "predicting the co evolution of event and knowledge graphs"
  Abstract: Embedding learning, a.k.a. representation learning, has been shown to be able to model large-scale semantic knowledge graphs. A key concept is a mapping of the knowledge graph to a tensor representation whose entries are predicted by models using latent representations of generalized entities. Knowledge graphs are typically treated as static: A knowledge graph grows more links when more facts become available but the ground truth values associated with links is considered time invariant. In this paper we address the issue of knowledge graphs where triple states depend on time. We assume that changes in the knowledge graph always arrive in form of events, in the sense that the events are the gateway to the knowledge graph. We train an event prediction model which uses both knowledge graph background information and information on recent events. By predicting future events, we also predict likely changes in the knowledge graph and thus obtain a model for the evolution of the knowledge graph as well. Our experiments demonstrate that our approach performs well in a clinical application, a recommendation engine and a sensor network application.

Connected through hyperedge HE-1 (induced by source paper "a survey on knowledge graph embeddings with literals which model links better literal ly"):
  with members:
    - "incorporating literals into knowledge graph embeddings"
    - "co training embeddings of knowledge graphs and entity descriptions for cross lingual entity alignment"
    - "embedding multimodal relational data for knowledge base completion"
    - "a first experiment on including text literals in kglove"
    - "kblrn end to end learning of knowledge base representations with latent relational and numerical features"
    - "multi task neural network for non discrete attribute prediction in knowledge graphs"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"
    - "chains of reasoning over entities relations and text using recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "learning sequence encoders for temporal knowledge graph completion"):
  with members:
    - "kblrn end to end learning of knowledge base representations with latent relational and numerical features"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"
    - "knowledge base completion baselines strike back"
    - "character aware neural language models"
    - "traversing knowledge graphs in vector space"
    - "adam a method for stochastic optimization"
    - "learning multi relational semantics using neural embedding models"

Connected through hyperedge HE-3 (induced by source paper "know evolve deep temporal reasoning for dynamic knowledge graphs"):
  with members:
    - "probabilistic reasoning via deep learning neural association models"
    - "steering opinion dynamics in information diffusion networks"
    - "discriminative embeddings of latent variable models for structured data"
    - "deep coevolutionary network embedding user and item features for recommendation"
    - "session based recommendations with recurrent neural networks"
    - "coevolve a joint point process model for information diffusion and network co evolution"
    - "shaping social activity by incentivizing users"
    - "embedding entities and relations for learning and inference in knowledge bases"

Connected through hyperedge HE-4 (induced by source paper "the tensor memory hypothesis"):
  with members:
    - "improving visual relationship detection using semantic modeling of scene descriptions"
    - "predictive clinical decision support system with rnn encoding and tensor decoding"
    - "dense associative memory for pattern recognition"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "learning with memory embeddings"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sum product networks a new deep architecture"

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)