human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "recurrent poisson factorization for temporal recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a collaborative filtering model with heterogeneous neural networks for recommender systems")
  Member "distilling information reliability and source trustworthiness from digital traces":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "intensity free learning of temporal point processes")
  Member "modeling the intensity function of point process via recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transformer hawkes process")
  Member "multistage campaigning in social networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "meta learning with relational information for short sequences")
  Member "learning granger causality for hawkes processes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning multivariate hawkes processes at scale")
  Member "retain interpretable predictive model in healthcare using reverse time attention mechanism":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bias in data driven ai systems an introductory survey")
  Member "recurrent neural networks for driver activity anticipation via sensory fusion architecture":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sensor fusion gated recurrent fusion to learn driving behavior from temporal multimodal data")
  Member "show attend and tell neural image caption generation with visual attention":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "relationship embedded representation learning for grounding referring expressions")

Question: Please tell me which class the source paper 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)