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: "interaction aware multi agent tracking and probabilistic behavior prediction via adversarial learning"
  Abstract: In order to enable high-quality decision making and motion planning of intelligent systems such as robotics and autonomous vehicles, accurate probabilistic predictions for surrounding interactive objects is a crucial prerequisite. Although many research studies have been devoted to making predictions on a single entity, it remains an open challenge to forecast future behaviors for multiple interactive agents simultaneously. In this work, we take advantage of the Generative Adversarial Network (GAN) due to its capability of distribution learning and propose a generic multi-agent probabilistic prediction and tracking framework which takes the interactions among multiple entities into account, in which all the entities are treated as a whole. However, since GAN is very hard to train, we make an empirical research and present the relationship between training performance and hyperparameter values with a numerical case study. The results imply that the proposed model can capture both the mean, variance and multi-modalities of the groundtruth distribution. Moreover, we apply the proposed approach to a real-world task of vehicle behavior prediction to demonstrate its effectiveness and accuracy. The results illustrate that the proposed model trained by adversarial learning can achieve a better prediction performance than other state-of-the-art models trained by traditional supervised learning which maximizes the data likelihood. The well-trained model can also be utilized as an implicit proposal distribution for particle filtered based Bayesian state estimation.

Connected through hyperedge HE-1 (induced by source paper "social wagdat interaction aware trajectory prediction via wasserstein graph double attention network"):
  with members:
    - "uncertainty aware driver trajectory prediction at urban intersections"
    - "social ways learning multi modal distributions of pedestrian trajectories with gans"
    - "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"
    - "human motion trajectory prediction a survey"
    - "looking to relations for future trajectory forecast"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "potential field interpretable and unified representation for trajectory prediction"
    - "interaction dataset an international adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps"

Connected through hyperedge HE-2 (induced by source paper "cmts conditional multiple trajectory synthesizer for generating safety critical driving scenarios"):
  with members:
    - "fully featured attribute transfer"
    - "a multi domain feature learning method for visual place recognition"
    - "a closed form solution to universal style transfer"
    - "online vehicle trajectory prediction using policy anticipation network and optimization based context reasoning"
    - "interpolation in generative models"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "predicting vehicle behaviors over an extended horizon using behavior interaction network"
    - "interpreting the latent space of gans for semantic face editing"

Connected through hyperedge HE-3 (induced by source paper "conditional generative neural system for probabilistic trajectory prediction"):
  with members:
    - "generic tracking and probabilistic prediction framework and its application in autonomous driving"
    - "coordination and trajectory prediction for vehicle interactions via bayesian generative modeling"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "generic probabilistic interactive situation recognition and prediction from virtual to real"
    - "learning and inferring a driver s braking action in car following scenarios"
    - "generic vehicle tracking framework capable of handling occlusions based on modified mixture particle filter"
    - "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning"
    - "social gan socially acceptable trajectories with generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "diversity aware vehicle motion prediction via latent semantic sampling"):
  with members:
    - "uncertainty aware driver trajectory prediction at urban intersections"
    - "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"
    - "multiple futures prediction"
    - "analyzing the variety loss in the context of probabilistic trajectory prediction"
    - "efficient autonomy validation in simulation with adaptive stress testing"
    - "disentangled relational representations for explaining and learning from demonstration"
    - "interpreting the latent space of gans for semantic face editing"
    - "conditional generative neural system for probabilistic trajectory prediction"

Connected through hyperedge HE-5 (induced by source paper "hcnaf hyper conditioned neural autoregressive flow and its application for probabilistic occupancy map forecasting"):
  with members:
    - "multiple futures prediction"
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "stargan unified generative adversarial networks for multi domain image to image translation"

Connected through hyperedge HE-6 (induced by source paper "generic tracking and probabilistic prediction framework and its application in autonomous driving"):
  with members:
    - "coordination and trajectory prediction for vehicle interactions via bayesian generative modeling"
    - "conditional generative neural system for probabilistic trajectory prediction"
    - "generic probabilistic interactive situation recognition and prediction from virtual to real"
    - "learning and inferring a driver s braking action in car following scenarios"
    - "generic vehicle tracking framework capable of handling occlusions based on modified mixture particle filter"
    - "dynamic occupancy grid prediction for urban autonomous driving a deep learning approach with fully automatic labeling"
    - "desire distant future prediction in dynamic scenes with interacting agents"

Connected through hyperedge HE-7 (induced by source paper "coordination and trajectory prediction for vehicle interactions via bayesian generative modeling"):
  with members:
    - "uncertainty aware driver trajectory prediction at urban intersections"
    - "generic probabilistic interactive situation recognition and prediction from virtual to real"
    - "generic vehicle tracking framework capable of handling occlusions based on modified mixture particle filter"
    - "probabilistic prediction of interactive driving behavior via hierarchical inverse reinforcement learning"
    - "courteous autonomous cars"
    - "bayesian conditional generative adverserial networks"
    - "conditional generative adversarial nets"

Connected through hyperedge HE-8 (induced by source paper "longitudinal trajectory forecasting of human driven vehicles near traffic lights using vehicle communications"):
  with members:
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "adam a method for stochastic optimization"

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.RO(Robotics)