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 temporal point processes via reinforcement learning"
  Abstract: Social goods, such as healthcare, smart city, and information networks, often produce ordered event data in continuous time. The generative processes of these event data can be very complex, requiring flexible models to capture their dynamics. Temporal point processes offer an elegant framework for modeling event data without discretizing the time. However, the existing maximum-likelihood-estimation (MLE) learning paradigm requires hand-crafting the intensity function beforehand and cannot directly monitor the goodness-of-fit of the estimated model in the process of training. To alleviate the risk of model-misspecification in MLE, we propose to generate samples from the generative model and monitor the quality of the samples in the process of training until the samples and the real data are indistinguishable. We take inspiration from reinforcement learning (RL) and treat the generation of each event as the action taken by a stochastic policy. We parameterize the policy as a flexible recurrent neural network and gradually improve the policy to mimic the observed event distribution. Since the reward function is unknown in this setting, we uncover an analytic and nonparametric form of the reward function using an inverse reinforcement learning formulation. This new RL framework allows us to derive an efficient policy gradient algorithm for learning flexible point process models, and we show that it performs well in both synthetic and real data.

Connected through hyperedge HE-1 (induced by source paper "on computation and generalization of generative adversarial imitation learning"):
  with members:
    - "on the global convergence of imitation learning a case for linear quadratic regulator"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "imitating driver behavior with generative adversarial networks"
    - "mmd gan towards deeper understanding of moment matching network"
    - "generalization and equilibrium in generative adversarial nets gans"
    - "proximal policy optimization algorithms"
    - "generative adversarial imitation learning"
    - "guided cost learning deep inverse optimal control via policy optimization"

Connected through hyperedge HE-2 (induced by source paper "transformer hawkes process"):
  with members:
    - "supervised reinforcement learning with recurrent neural network for dynamic treatment recommendation"
    - "self attention with relative position representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "comparative study of cnn and rnn for natural language processing"
    - "convolutional sequence to sequence learning"
    - "modeling the intensity function of point process via recurrent neural networks"
    - "wasserstein learning of deep generative point process models"

Connected through hyperedge HE-3 (induced by source paper "modeling of spatio temporal hawkes processes with randomized kernels"):
  with members:
    - "reinforcement learning of spatio temporal point processes"
    - "multivariate spatiotemporal hawkes processes and network reconstruction"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "evolutionary dynamics of information diffusion over social networks"
    - "modeling heterogeneous network interference using poisson point processes"

Connected through hyperedge HE-4 (induced by source paper "interpretable generative neural spatio temporal point processes"):
  with members:
    - "wasserstein learning of deep generative point process models"
    - "the neural hawkes process a neurally self modulating multivariate point process"
    - "benchmarking deep reinforcement learning for continuous control"
    - "a kernel method for the two sample problem"

Connected through hyperedge HE-5 (induced by source paper "time2vec learning a vector representation of time"):
  with members:
    - "relational representation learning for dynamic knowledge graphs a survey"
    - "retainvis visual analytics with interpretable and interactive recurrent neural networks on electronic medical records"
    - "neural decomposition of time series data for effective generalization"
    - "can recurrent neural networks warp time"
    - "deep reinforcement learning of marked temporal point processes"
    - "streaming graph neural networks"
    - "neural ordinary differential equations"
    - "learning dynamic embeddings from temporal interactions"

Connected through hyperedge HE-6 (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"
    - "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"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "intensity free learning of temporal point processes"):
  with members:
    - "predicting dynamic embedding trajectory in temporal interaction networks"
    - "sum of squares polynomial flow"
    - "uncertainty on asynchronous time event prediction"
    - "fully neural network based model for general temporal point processes"
    - "pyro deep universal probabilistic programming"
    - "deep reinforcement learning of marked temporal point processes"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"

Connected through hyperedge HE-8 (induced by source paper "point process flows"):
  with members:
    - "a variational auto encoder model for stochastic point processes"
    - "stable architectures for deep neural networks"
    - "time perception machine temporal point processes for the when where and what of activity prediction"
    - "neural ordinary differential equations"
    - "beyond finite layer neural networks bridging deep architectures and numerical differential equations"
    - "reversible architectures for arbitrarily deep residual neural networks"
    - "wasserstein learning of deep generative point process models"
    - "the neural hawkes process a neurally self modulating multivariate point process"

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)