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: "wasserstein learning of deep generative point process models"
  Abstract: Point processes are becoming very popular in modeling asynchronous sequential data due to their sound mathematical foundation and strength in modeling a variety of real-world phenomena. Currently, they are often characterized via intensity function which limits model's expressiveness due to unrealistic assumptions on its parametric form used in practice. Furthermore, they are learned via maximum likelihood approach which is prone to failure in multi-modal distributions of sequences. In this paper, we propose an intensity-free approach for point processes modeling that transforms nuisance processes to a target one. Furthermore, we train the model using a likelihood-free leveraging Wasserstein distance between point processes. Experiments on various synthetic and real-world data substantiate the superiority of the proposed point process model over conventional ones.

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"
    - "modeling the intensity function of point process via recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "learning multivariate hawkes processes at scale"):
  with members:
    - "predicting dynamic embedding trajectory in temporal interaction networks"
    - "latent odes for irregularly sampled time series"
    - "community interaction and conflict on the web"
    - "sir hawkes linking epidemic models and hawkes processes to model diffusions in finite populations"
    - "fake news mitigation via point process based intervention"
    - "learning from clinical judgments semi markov modulated marked hawkes processes for risk prognosis"
    - "learning granger causality for hawkes processes"
    - "seismic a self exciting point process model for predicting tweet popularity"

Connected through hyperedge HE-3 (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"
    - "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-4 (induced by source paper "interpretable generative neural spatio temporal point processes"):
  with members:
    - "learning temporal point processes via reinforcement learning"
    - "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"
    - "learning temporal point processes via reinforcement learning"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "augmenting correlation structures in spatial data using deep generative models"):
  with members:
    - "generative adversarial networks for financial trading strategies fine tuning and combination"
    - "coco gan generation by parts via conditional coordinating"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "evolutionary generative adversarial networks"
    - "tile2vec unsupervised representation learning for spatially distributed data"
    - "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration"
    - "point cloud gan"

Connected through hyperedge HE-7 (induced by source paper "hawkes process for understanding the influence of pathogenic social media accounts"):
  with members:
    - "privacy in social media identification mitigation and applications"
    - "similar but different exploiting users congruity for recommendation systems"
    - "protecting user privacy an approach for untraceable web browsing history and unambiguous user profiles"
    - "early identification of pathogenic social media accounts"
    - "securing social media user data an adversarial approach"
    - "the spread of low credibility content by social bots"
    - "semi supervised learning for detecting human trafficking"
    - "the spread of fake news by social bots"

Connected through hyperedge HE-8 (induced by source paper "a variational auto encoder model for stochastic point processes"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "probabilistic video generation using holistic attribute control"
    - "stochastic video generation with a learned prior"
    - "time perception machine temporal point processes for the when where and what of activity prediction"
    - "when will you do what anticipating temporal occurrences of activities"
    - "disentangled sequential autoencoder"
    - "classify predict detect anticipate and synthesize hierarchical recurrent latent variable models for human activity modeling"
    - "a probabilistic semi supervised approach to multi task human activity modeling"

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)