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: "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.

Connected through hyperedge HE-1 (induced by source paper "disentangling physical dynamics from unknown factors for unsupervised video prediction"):
  with members:
    - "differentiable physics informed graph networks"
    - "improved conditional vrnns for video prediction"
    - "unsupervised learning of object structure and dynamics from videos"
    - "compositional video prediction"
    - "pde net 2 0 learning pdes from data with a numeric symbolic hybrid deep network"
    - "recurrent kalman networks factorized inference in high dimensional deep feature spaces"
    - "memory in memory a predictive neural network for learning higher order non stationarity from spatiotemporal dynamics"
    - "hybridnet classification and reconstruction cooperation for semi supervised learning"

Connected through hyperedge HE-2 (induced by source paper "dissecting neural odes"):
  with members:
    - "universal differential equations for scientific machine learning"
    - "imexnet a forward stable deep neural network"
    - "anodev2 a coupled neural ode evolution framework"
    - "deep learning via dynamical systems an approximation perspective"
    - "state space representations of deep neural networks"
    - "neural stochastic differential equations deep latent gaussian models in the diffusion limit"
    - "deep equilibrium models"
    - "port hamiltonian approach to neural network training"

Connected through hyperedge HE-3 (induced by source paper "scalable gradients for stochastic differential equations"):
  with members:
    - "neural sde stabilizing neural ode networks with stochastic noise"
    - "latent odes for irregularly sampled time series"
    - "neural stochastic differential equations deep latent gaussian models in the diffusion limit"
    - "a differentiable programming system to bridge machine learning and scientific computing"
    - "deep learning with differential gaussian process flows"
    - "stable architectures for deep neural networks"
    - "deep neural networks motivated by partial differential equations"
    - "ffjord free form continuous dynamics for scalable reversible generative models"

Connected through hyperedge HE-4 (induced by source paper "stable neural flows"):
  with members:
    - "universal differential equations for scientific machine learning"
    - "dissecting neural odes"
    - "scalable gradients for stochastic differential equations"
    - "a survey of optimization methods from a machine learning perspective"
    - "graph neural ordinary differential equations"
    - "stable architectures for deep neural networks"
    - "beyond finite layer neural networks bridging deep architectures and numerical differential equations"
    - "transport analysis of infinitely deep neural network"

Connected through hyperedge HE-5 (induced by source paper "neural stochastic differential equations deep latent gaussian models in the diffusion limit"):
  with members:
    - "neural sde stabilizing neural ode networks with stochastic noise"
    - "diffeqflux jl a julia library for neural differential equations"
    - "deep learning with differential gaussian process flows"
    - "stable architectures for deep neural networks"
    - "neural ordinary differential equations"
    - "maximum principle based algorithms for deep learning"
    - "identity matters in deep learning"
    - "automatic differentiation in machine learning a survey"

Connected through hyperedge HE-6 (induced by source paper "deeponet learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators"):
  with members:
    - "gmls nets a framework for learning from unstructured data"
    - "deepxde a deep learning library for solving differential equations"
    - "attention is all you need"
    - "dynamic routing between capsules"
    - "functional multi layer perceptron a non linear tool for functional data analysis"

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)