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: "learning network of multivariate hawkes processes a time series approach"
  Abstract: Learning the influence structure of multiple time series data is of great interest to many disciplines. This paper studies the problem of recovering the causal structure in network of multivariate linear Hawkes processes. In such processes, the occurrence of an event in one process affects the probability of occurrence of new events in some other processes. Thus, a natural notion of causality exists between such processes captured by the support of the excitation matrix. We show that the resulting causal influence network is equivalent to the Directed Information graph (DIG) of the processes, which encodes the causal factorization of the joint distribution of the processes. Furthermore, we present an algorithm for learning the support of excitation matrix (or equivalently the DIG). The performance of the algorithm is evaluated on synthesized multivariate Hawkes networks as well as a stock market and MemeTracker real-world dataset.

Connected through hyperedge HE-1 (induced by source paper "self attentive hawkes processes"):
  with members:
    - "self attention with structural position representations"
    - "intensity free learning of temporal point processes"
    - "fully neural network based model for general temporal point processes"
    - "learning temporal point processes via reinforcement learning"
    - "self attention with relative position representations"
    - "benchmarking deep sequential models on volatility predictions for financial time series"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "non local neural networks"

Connected through hyperedge HE-2 (induced by source paper "learning hawkes processes from a handful of events"):
  with members:
    - "fast estimation of causal interactions using wold processes"
    - "bayesian paragraph vectors"
    - "advances in variational inference"
    - "bayesian neural word embedding"
    - "learning granger causality for hawkes processes"
    - "directed information graphs"

Connected through hyperedge HE-3 (induced by source paper "fast estimation of causal interactions using wold processes"):
  with members:
    - "network inference via the time varying graphical lasso"
    - "motifs in temporal networks"
    - "learning granger causality for hawkes processes"
    - "microscopic approach of a time elapsed neural model"
    - "a scalable asynchronous distributed algorithm for topic modeling"

Connected through hyperedge HE-4 (induced by source paper "automatic decomposition of self triggering kernels of hawkes processes"):
  with members:
    - "the neural hawkes process a neurally self modulating multivariate point process"
    - "tideh time dependent hawkes process for predicting retweet dynamics"
    - "learning granger causality for hawkes processes"
    - "seismic a self exciting point process model for predicting tweet popularity"

Connected through hyperedge HE-5 (induced by source paper "interaction information for causal inference the case of directed triangle"):
  with members:
    - "entropic causal inference"

Connected through hyperedge HE-6 (induced by source paper "the neural hawkes process a neurally self modulating multivariate point process"):
  with members:
    - "joint modeling of event sequence and time series with attentional twin recurrent neural networks"
    - "wasserstein learning of deep generative point process models"
    - "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)