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 dynamic embedding trajectory in temporal interaction networks"
  Abstract: Modeling sequential interactions between users and items/products is crucial in domains such as e-commerce, social networking, and education. Representation learning presents an attractive opportunity to model the dynamic evolution of users and items, where each user/item can be embedded in a Euclidean space and its evolution can be modeled by an embedding trajectory in this space. However, existing dynamic embedding methods generate embeddings only when users take actions and do not explicitly model the future trajectory of the user/item in the embedding space. Here we propose JODIE, a coupled recurrent neural network model that learns the embedding trajectories of users and items. JODIE employs two recurrent neural networks to update the embedding of a user and an item at every interaction. Crucially, JODIE also models the future embedding trajectory of a user/item. To this end, it introduces a novel projection operator that learns to estimate the embedding of the user at any time in the future. These estimated embeddings are then used to predict future user-item interactions. To make the method scalable, we develop a t-Batch algorithm that creates time-consistent batches and leads to 9x faster training. We conduct six experiments to validate JODIE on two prediction tasks---future interaction prediction and state change prediction---using four real-world datasets. We show that JODIE outperforms six state-of-the-art algorithms in these tasks by at least 20% in predicting future interactions and 12% in state change prediction.

Connected through hyperedge HE-1 (induced by source paper "learning multivariate hawkes processes at scale"):
  with members:
    - "latent odes for irregularly sampled time series"
    - "community interaction and conflict on the web"
    - "sir hawkes linking epidemic models and hawkes processes to model diffusions in finite populations"
    - "fake news mitigation via point process based intervention"
    - "wasserstein learning of deep generative point process models"
    - "learning from clinical judgments semi markov modulated marked hawkes processes for risk prognosis"
    - "learning granger causality for hawkes processes"
    - "seismic a self exciting point process model for predicting tweet popularity"

Connected through hyperedge HE-2 (induced by source paper "higher order label homogeneity and spreading in graphs"):
  with members:
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "higher order ranking and link prediction from closing triangles to closing higher order motifs"
    - "higher order clustering in networks"
    - "simplicial closure and higher order link prediction"
    - "higher order organization of complex networks"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "revisiting semi supervised learning with graph embeddings"

Connected through hyperedge HE-3 (induced by source paper "ties temporal interaction embeddings for enhancing social media integrity at facebook"):
  with members:
    - "a survey of the recent architectures of deep convolutional neural networks"
    - "pytorch biggraph a large scale graph embedding system"
    - "poised spotting twitter spam off the beaten paths"
    - "deep learning for hate speech detection in tweets"
    - "attention is all you need"
    - "in a world that counts clustering and detecting fake social engagement at scale"
    - "a critical review of recurrent neural networks for sequence learning"

Connected through hyperedge HE-4 (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"
    - "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"
    - "learning sequence encoders for temporal knowledge graph completion"

Connected through hyperedge HE-5 (induced by source paper "intensity free learning of temporal point processes"):
  with members:
    - "sum of squares polynomial flow"
    - "uncertainty on asynchronous time event prediction"
    - "fully neural network based model for general temporal point processes"
    - "learning temporal point processes via reinforcement learning"
    - "pyro deep universal probabilistic programming"
    - "deep reinforcement learning of marked temporal point processes"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"

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"
    - "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"
    - "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.SI(Social and Information Networks)