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 "neural jump stochastic differential equations"):
  Abstract: Many time series can be effectively modeled with a combination of continuous flows along with random jumps sparked by discrete events. However, we usually do not have the equation of motion describing the flows, or how they are affected by jumps. To this end, we introduce Neural Jump Stochastic Differential Equations that provide a data-driven approach to learn continuous and discrete dynamic behavior, i.e., hybrid systems that both flow and jump. Our approach extends the framework of Neural Ordinary Differential Equations with a stochastic process term that models discrete events. We then model temporal point processes with a piecewise-continuous latent trajectory, where stochastic events cause an abrupt change in the latent variables. We demonstrate the predictive capabilities of our model on a range of synthetic and real-world marked point process datasets, including classical point processes such as Hawkes processes, medical records, awards on Stack Overflow, and earthquake monitoring.

Includes members:
  Member "latent odes for irregularly sampled time series":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fundamentals of recurrent neural network rnn and long short term memory lstm network")
  Member "diffeqflux jl a julia library for neural differential equations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approximation capabilities of neural odes and invertible residual networks")
  Member "neural stochastic differential equations deep latent gaussian models in the diffusion limit":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dissecting neural odes")
  Member "a differentiable programming system to bridge machine learning and scientific computing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "differentiate everything with a reversible programming language")
  Member "hidden physics models machine learning of nonlinear partial differential equations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hp vpinns variational physics informed neural networks with domain decomposition")
  Member "solving for high dimensional committor functions using artificial neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "hp vpinns variational physics informed neural networks with domain decomposition")
  Member "a comparison of automatic differentiation and continuous sensitivity analysis for derivatives of differential equation solutions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "automatic differentiation and continuous sensitivity analysis of rigid body dynamics")
  Member "expecting to be hip hawkes intensity processes for social media popularity":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "nestpp modeling thread dynamics in online discussion forums")

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)