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 "learning hawkes processes from a handful of events"):
  Abstract: Learning the causal-interaction network of multivariate Hawkes processes is a useful task in many applications. Maximum-likelihood estimation is the most common approach to solve the problem in the presence of long observation sequences. However, when only short sequences are available, the lack of data amplifies the risk of overfitting and regularization becomes critical. Due to the challenges of hyper-parameter tuning, state-of-the-art methods only parameterize regularizers by a single shared hyper-parameter, hence limiting the power of representation of the model. To solve both issues, we develop in this work an efficient algorithm based on variational expectation-maximization. Our approach is able to optimize over an extended set of hyper-parameters. It is also able to take into account the uncertainty in the model parameters by learning a posterior distribution over them. Experimental results on both synthetic and real datasets show that our approach significantly outperforms state-of-the-art methods under short observation sequences.

Includes members:
  Member "fast estimation of causal interactions using wold processes":
    also connected through other hyperedges:
      (none)
  Member "bayesian paragraph vectors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "identification interpretability and bayesian word embeddings")
  Member "advances in variational inference":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial disentanglement with grouped observations")
  Member "bayesian neural word embedding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "attentive item2vec neural attentive user representations")
  Member "learning network of multivariate hawkes processes a time series approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "self attentive hawkes processes")
  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 "directed information graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "variable lag granger causality and transfer entropy for time series analysis")

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)