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: "joint modeling of event sequence and time series with attentional twin recurrent neural networks"
  Abstract: A variety of real-world processes (over networks) produce sequences of data whose complex temporal dynamics need to be studied. More especially, the event timestamps can carry important information about the underlying network dynamics, which otherwise are not available from the time-series evenly sampled from continuous signals. Moreover, in most complex processes, event sequences and evenly-sampled times series data can interact with each other, which renders joint modeling of those two sources of data necessary. To tackle the above problems, in this paper, we utilize the rich framework of (temporal) point processes to model event data and timely update its intensity function by the synergic twin Recurrent Neural Networks (RNNs). In the proposed architecture, the intensity function is synergistically modulated by one RNN with asynchronous events as input and another RNN with time series as input. Furthermore, to enhance the interpretability of the model, the attention mechanism for the neural point process is introduced. The whole model with event type and timestamp prediction output layers can be trained end-to-end and allows a black-box treatment for modeling the intensity. We substantiate the superiority of our model in synthetic data and three real-world benchmark datasets.

Connected through hyperedge HE-1 (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"
    - "time dependent representation for neural event sequence prediction"
    - "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-2 (induced by source paper "set functions for time series"):
  with members:
    - "interpolation prediction networks for irregularly sampled time series"
    - "set transformer a framework for attention based permutation invariant neural networks"
    - "conditional neural processes"
    - "deep sets"
    - "attention is all you need"
    - "phased lstm accelerating recurrent network training for long or event based sequences"
    - "the neural hawkes process a neurally self modulating multivariate point process"
    - "directly modeling missing data in sequences with rnns improved classification of clinical time series"

Connected through hyperedge HE-3 (induced by source paper "carepre an intelligent clinical decision assistance system"):
  with members:
    - "retainvis visual analytics with interpretable and interactive recurrent neural networks on electronic medical records"
    - "eegnet a compact convolutional neural network for eeg based brain computer interfaces"
    - "cardiologist level arrhythmia detection with convolutional neural networks"
    - "multi task learning for mental health using social media text"
    - "towards better analysis of deep convolutional neural networks"
    - "retain interpretable predictive model in healthcare using reverse time attention mechanism"
    - "lstmvis a tool for visual analysis of hidden state dynamics in recurrent neural networks"
    - "doctor ai predicting clinical events via recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "the neural hawkes process a neurally self modulating multivariate point process"):
  with members:
    - "wasserstein learning of deep generative point process models"
    - "learning network of multivariate hawkes processes a time series approach"
    - "learning granger causality for hawkes processes"
    - "visualizing and understanding recurrent networks"
    - "seismic a self exciting point process model for predicting tweet popularity"
    - "adam a method for stochastic optimization"
    - "one billion word benchmark for measuring progress in statistical language modeling"
    - "structure and dynamics of information pathways in online media"

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)