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: "modeling the intensity function of point process via recurrent neural networks"
  Abstract: Event sequence, asynchronously generated with random timestamp, is ubiquitous among applications. The precise and arbitrary timestamp can carry important clues about the underlying dynamics, and has lent the event data fundamentally different from the time-series whereby series is indexed with fixed and equal time interval. One expressive mathematical tool for modeling event is point process. The intensity functions of many point processes involve two components: the background and the effect by the history. Due to its inherent spontaneousness, the background can be treated as a time series while the other need to handle the history events. In this paper, we model the background by a Recurrent Neural Network (RNN) with its units aligned with time series indexes while the history effect is modeled by another RNN whose units are aligned with asynchronous events to capture the long-range dynamics. The whole model with event type and timestamp prediction output layers can be trained end-to-end. Our approach takes an RNN perspective to point process, and models its background and history effect. For utility, our method allows a black-box treatment for modeling the intensity which is often a pre-defined parametric form in point processes. Meanwhile end-to-end training opens the venue for reusing existing rich techniques in deep network for point process modeling. We apply our model to the predictive maintenance problem using a log dataset by more than 1000 ATMs from a global bank headquartered in North America.

Connected through hyperedge HE-1 (induced by source paper "transformer hawkes process"):
  with members:
    - "learning temporal point processes via reinforcement learning"
    - "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"
    - "wasserstein learning of deep generative point process models"

Connected through hyperedge HE-2 (induced by source paper "a multi channel neural graphical event model with negative evidence"):
  with members:
    - "time dependent representation for neural event sequence prediction"
    - "wasserstein learning of deep generative point process models"
    - "the neural hawkes process a neurally self modulating multivariate point process"
    - "modeling events with cascades of poisson processes"
    - "ct nor representing and reasoning about events in continuous time"

Connected through hyperedge HE-3 (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-4 (induced by source paper "neural jump stochastic differential equations"):
  with members:
    - "latent odes for irregularly sampled time series"
    - "diffeqflux jl a julia library for neural differential equations"
    - "neural stochastic differential equations deep latent gaussian models in the diffusion limit"
    - "a differentiable programming system to bridge machine learning and scientific computing"
    - "hidden physics models machine learning of nonlinear partial differential equations"
    - "solving for high dimensional committor functions using artificial neural networks"
    - "a comparison of automatic differentiation and continuous sensitivity analysis for derivatives of differential equation solutions"
    - "expecting to be hip hawkes intensity processes for social media popularity"

Connected through hyperedge HE-5 (induced by source paper "modeling emotion in complex stories the stanford emotional narratives dataset"):
  with members:
    - "applying probabilistic programming to affective computing"
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "sewa db a rich database for audio visual emotion and sentiment research in the wild"
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "attending to emotional narratives"
    - "the omg emotion behavior dataset"
    - "a multimodal lstm for predicting listener empathic responses over time"
    - "multimodal generative models for scalable weakly supervised learning"

Connected through hyperedge HE-6 (induced by source paper "self attention with functional time representation learning"):
  with members:
    - "modeling complementary products and customer preferences with context knowledge for online recommendation"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "self attentive sequential recommendation"
    - "attention is all you need"
    - "time dependent representation for neural event sequence prediction"
    - "joint modeling of event sequence and time series with attentional twin recurrent neural networks"
    - "the neural hawkes process a neurally self modulating multivariate point process"
    - "density estimation using real nvp"

Connected through hyperedge HE-7 (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"
    - "deep reinforcement learning of marked temporal point processes"
    - "fake news mitigation via point process based intervention"
    - "a dirichlet mixture model of hawkes processes for event sequence clustering"
    - "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-8 (induced by source paper "learning latent process from high dimensional event sequences via efficient sampling"):
  with members:
    - "learning temporal point processes via reinforcement learning"
    - "deep reinforcement learning of marked temporal point processes"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "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"

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)