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: "deep reinforcement learning of marked temporal point processes"
  Abstract: In a wide variety of applications, humans interact with a complex environment by means of asynchronous stochastic discrete events in continuous time. Can we design online interventions that will help humans achieve certain goals in such asynchronous setting? In this paper, we address the above problem from the perspective of deep reinforcement learning of marked temporal point processes, where both the actions taken by an agent and the feedback it receives from the environment are asynchronous stochastic discrete events characterized using marked temporal point processes. In doing so, we define the agent's policy using the intensity and mark distribution of the corresponding process and then derive a flexible policy gradient method, which embeds the agent's actions and the feedback it receives into real-valued vectors using deep recurrent neural networks. Our method does not make any assumptions on the functional form of the intensity and mark distribution of the feedback and it allows for arbitrarily complex reward functions. We apply our methodology to two different applications in personalized teaching and viral marketing and, using data gathered from Duolingo and Twitter, we show that it may be able to find interventions to help learners and marketers achieve their goals more effectively than alternatives.

Connected through hyperedge HE-1 (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"
    - "learning temporal point processes via reinforcement learning"
    - "neural decomposition of time series data for effective generalization"
    - "can recurrent neural networks warp time"
    - "streaming graph neural networks"
    - "neural ordinary differential equations"
    - "learning dynamic embeddings from temporal interactions"

Connected through hyperedge HE-2 (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"
    - "learning temporal point processes via reinforcement learning"
    - "pyro deep universal probabilistic programming"
    - "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-3 (induced by source paper "reinforcement learning with policy mixture model for temporal point processes clustering"):
  with members:
    - "learning temporal point processes via reinforcement learning"
    - "decoupled learning for factorial marked temporal point processes"
    - "fake news mitigation via point process based intervention"
    - "a dirichlet mixture model of hawkes processes for event sequence clustering"
    - "modeling the intensity function of point process via recurrent neural networks"
    - "wasserstein learning of deep generative point process models"
    - "the neural hawkes process a neurally self modulating multivariate point process"
    - "generative adversarial imitation learning"

Connected through hyperedge HE-4 (induced by source paper "learning latent process from high dimensional event sequences via efficient sampling"):
  with members:
    - "learning temporal point processes via reinforcement learning"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "modeling the intensity function of point process via recurrent neural networks"
    - "wasserstein learning of deep generative point process models"
    - "generative adversarial imitation learning"
    - "learning granger causality for hawkes processes"
    - "deepcas an end to end predictor of information cascades"

Connected through hyperedge HE-5 (induced by source paper "reinforcement learning of spatio temporal point processes"):
  with members:
    - "learning temporal point processes via reinforcement learning"
    - "wasserstein learning of deep generative point process models"
    - "the neural hawkes process a neurally self modulating multivariate point process"
    - "generative adversarial imitation learning"
    - "benchmarking deep reinforcement learning for continuous control"
    - "a kernel method for the two sample problem"

Connected through hyperedge HE-6 (induced by source paper "learning to crawl"):
  with members:
    - "steering social activity a stochastic optimal control point of view"
    - "variational policy for guiding point processes"
    - "smart broadcasting do you want to be seen"
    - "multi player bandits a musical chairs approach"
    - "concurrent bandits and cognitive radio networks"

Connected through hyperedge HE-7 (induced by source paper "fully neural network based model for general temporal point processes"):
  with members:
    - "learning temporal point processes via reinforcement learning"
    - "modeling the intensity function of point process via recurrent neural networks"
    - "the neural hawkes process a neurally self modulating multivariate point process"
    - "automatic differentiation in machine learning a survey"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "non submodular function maximization subject to a matroid constraint with applications"):
  with members:
    - "submodular maximization beyond non negativity guarantees fast algorithms and applications"
    - "steering social activity a stochastic optimal control point of view"
    - "equity of attention amortizing individual fairness in rankings"
    - "fairness of exposure in rankings"
    - "combinatorial penalties which structures are preserved by convex relaxations"
    - "weakly submodular maximization beyond cardinality constraints does randomization help greedy"
    - "gradient methods for submodular maximization"
    - "guarantees for greedy maximization of non submodular functions with applications"

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)