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: "time dependent representation for neural event sequence prediction"
  Abstract: Existing sequence prediction methods are mostly concerned with time-independent sequences, in which the actual time span between events is irrelevant and the distance between events is simply the difference between their order positions in the sequence. While this time-independent view of sequences is applicable for data such as natural languages, e.g., dealing with words in a sentence, it is inappropriate and inefficient for many real world events that are observed and collected at unequally spaced points of time as they naturally arise, e.g., when a person goes to a grocery store or makes a phone call. The time span between events can carry important information about the sequence dependence of human behaviors. In this work, we propose a set of methods for using time in sequence prediction. Because neural sequence models such as RNN are more amenable for handling token-like input, we propose two methods for time-dependent event representation, based on the intuition on how time is tokenized in everyday life and previous work on embedding contextualization. We also introduce two methods for using next event duration as regularization for training a sequence prediction model. We discuss these methods based on recurrent neural nets. We evaluate these methods as well as baseline models on five datasets that resemble a variety of sequence prediction tasks. The experiments revealed that the proposed methods offer accuracy gain over baseline models in a range of settings.

Connected through hyperedge HE-1 (induced by source paper "d ej a vu a contextualized temporal attention mechanism for sequential recommendation"):
  with members:
    - "bert4rec sequential recommendation with bidirectional encoder representations from transformer"
    - "transformer xl attentive language models beyond a fixed length context"
    - "towards neural mixture recommender for long range dependent user sequences"
    - "hierarchical temporal convolutional networks for dynamic recommender systems"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "time is of the essence a joint hierarchical rnn and point process model for time and item predictions"

Connected through hyperedge HE-2 (induced by source paper "a multi channel neural graphical event model with negative evidence"):
  with members:
    - "modeling the intensity function of point process via recurrent neural networks"
    - "wasserstein learning of deep generative point process models"
    - "the neural hawkes process a neurally self modulating multivariate point process"
    - "modeling events with cascades of poisson processes"
    - "ct nor representing and reasoning about events in continuous time"

Connected through hyperedge HE-3 (induced by source paper "time2vec learning a vector representation of time"):
  with members:
    - "relational representation learning for dynamic knowledge graphs a survey"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "spatial temporal convolutional lstms for tumor growth prediction by learning 4d longitudinal patient data"):
  with members:
    - "convolutional invasion and expansion networks for tumor growth prediction"
    - "predicting deeper into the future of semantic segmentation"
    - "decomposing motion and content for natural video sequence prediction"
    - "learning to generate long term future via hierarchical prediction"
    - "improving deep pancreas segmentation in ct and mri images via recurrent neural contextual learning and direct loss function"
    - "joint sequence learning and cross modality convolution for 3d biomedical segmentation"
    - "personalized pancreatic tumor growth prediction via group learning"
    - "future frame prediction for anomaly detection a new baseline"

Connected through hyperedge HE-5 (induced by source paper "self attention with functional time representation learning"):
  with members:
    - "modeling complementary products and customer preferences with context knowledge for online recommendation"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "self attentive sequential recommendation"
    - "attention is all you need"
    - "joint modeling of event sequence and time series with attentional twin recurrent neural networks"
    - "modeling the intensity function of point process via recurrent neural networks"
    - "the neural hawkes process a neurally self modulating multivariate point process"
    - "density estimation using real nvp"

Connected through hyperedge HE-6 (induced by source paper "explainable failure predictions with rnn classifiers based on time series data"):
  with members:
    - "the challenge of crafting intelligible intelligence"
    - "explainable time series tweaking via irreversible and reversible temporal transformations"
    - "lstm fully convolutional networks for time series classification"
    - "anomaly detection using one class neural networks"
    - "interpretable categorization of heterogeneous time series data"
    - "interpretable explorable approximations of black box models"
    - "attention is all you need"
    - "a glass box interactive machine learning approach for solving np hard problems with the human in the loop"

Connected through hyperedge HE-7 (induced by source paper "uncertainty on asynchronous time event prediction"):
  with members:
    - "bayesian recurrent neural networks"
    - "phased lstm accelerating recurrent network training for long or event based sequences"
    - "the neural hawkes process a neurally self modulating multivariate point process"
    - "adam a method for stochastic optimization"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "on prediction using variable order markov models"

Connected through hyperedge HE-8 (induced by source paper "neural ordinary differential equations"):
  with members:
    - "hidden physics models machine learning of nonlinear partial differential equations"
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"
    - "latent space physics towards learning the temporal evolution of fluid flow"
    - "the reversible residual network backpropagation without storing activations"
    - "optnet differentiable optimization as a layer in neural networks"
    - "beyond finite layer neural networks bridging deep architectures and numerical differential equations"
    - "reversible architectures for arbitrarily deep residual 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)