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: "the neural hawkes process a neurally self modulating multivariate point process"
  Abstract: Many events occur in the world. Some event types are stochastically excited or inhibited---in the sense of having their probabilities elevated or decreased---by patterns in the sequence of previous events. Discovering such patterns can help us predict which type of event will happen next and when. We model streams of discrete events in continuous time, by constructing a neurally self-modulating multivariate point process in which the intensities of multiple event types evolve according to a novel continuous-time LSTM. This generative model allows past events to influence the future in complex and realistic ways, by conditioning future event intensities on the hidden state of a recurrent neural network that has consumed the stream of past events. Our model has desirable qualitative properties. It achieves competitive likelihood and predictive accuracy on real and synthetic datasets, including under missing-data conditions.

Connected through hyperedge HE-1 (induced by source paper "d ej a vu a contextualized temporal attention mechanism for sequential recommendation"):
  with members:
    - "bert4rec sequential recommendation with bidirectional encoder representations from transformer"
    - "transformer xl attentive language models beyond a fixed length context"
    - "towards neural mixture recommender for long range dependent user sequences"
    - "hierarchical temporal convolutional networks for dynamic recommender systems"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "recurrent neural networks with top k gains for session based recommendations"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "time is of the essence a joint hierarchical rnn and point process model for time and item predictions"

Connected through hyperedge HE-2 (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"
    - "modeling the intensity function of point process via recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "cause learning granger causality from event sequences using attribution methods"):
  with members:
    - "is attention interpretable"
    - "attention is not explanation"
    - "axiomatic attribution for deep networks"
    - "learning important features through propagating activation differences"
    - "towards better understanding of gradient based attribution methods for deep neural networks"
    - "learning granger causality for hawkes processes"
    - "show attend and tell neural image caption generation with visual attention"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-4 (induced by source paper "progressive growing of neural odes"):
  with members:
    - "latent odes for irregularly sampled time series"
    - "interpolation prediction networks for irregularly sampled time series"
    - "neural ordinary differential equations"
    - "teacher student curriculum learning"
    - "progressive growing of gans for improved quality stability and variation"
    - "directly modeling missing data in sequences with rnns improved classification of clinical time series"
    - "learning to execute"

Connected through hyperedge HE-5 (induced by source paper "a multi channel neural graphical event model with negative evidence"):
  with members:
    - "time dependent representation for neural event sequence prediction"
    - "modeling the intensity function of point process via recurrent neural networks"
    - "wasserstein learning of deep generative point process models"
    - "modeling events with cascades of poisson processes"
    - "ct nor representing and reasoning about events in continuous time"

Connected through hyperedge HE-6 (induced by source paper "interpretable generative neural spatio temporal point processes"):
  with members:
    - "learning temporal point processes via reinforcement learning"
    - "wasserstein learning of deep generative point process models"
    - "benchmarking deep reinforcement learning for continuous control"
    - "a kernel method for the two sample problem"

Connected through hyperedge HE-7 (induced by source paper "learning dynamic and personalized comorbidity networks from event data using deep diffusion processes"):
  with members:
    - "phased lstm accelerating recurrent network training for long or event based sequences"
    - "inferring networks of diffusion and influence"
    - "on the convexity of latent social network inference"

Connected through hyperedge HE-8 (induced by source paper "detection of outlier events in continuous time event sequences"):
  with members:
    - "imputing missing events in continuous time event streams"
    - "learning hawkes processes from short doubly censored event sequences"
    - "learning granger causality for hawkes processes"

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)