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: "social ways learning multi modal distributions of pedestrian trajectories with gans"
  Abstract: This paper proposes a novel approach for predicting the motion of pedestrians interacting with others. It uses a Generative Adversarial Network (GAN) to sample plausible predictions for any agent in the scene. As GANs are very susceptible to mode collapsing and dropping, we show that the recently proposed Info-GAN allows dramatic improvements in multi-modal pedestrian trajectory prediction to avoid these issues. We also left out L2-loss in training the generator, unlike some previous works, because it causes serious mode collapsing though faster convergence. #R##N#We show through experiments on real and synthetic data that the proposed method leads to generate more diverse samples and to preserve the modes of the predictive distribution. In particular, to prove this claim, we have designed a toy example dataset of trajectories that can be used to assess the performance of different methods in preserving the predictive distribution modes.

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"
    - "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"
    - "rules of the road predicting driving behavior with a convolutional model of semantic interactions"

Connected through hyperedge HE-2 (induced by source paper "a novel graph based trajectory predictor with pseudo oracle"):
  with members:
    - "situation aware pedestrian trajectory prediction with spatio temporal attention model"
    - "drogon a causal reasoning framework for future trajectory forecast"
    - "social and scene aware trajectory prediction in crowded spaces"
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "diverse trajectory forecasting with determinantal point processes"
    - "multiple futures prediction"
    - "analyzing the variety loss in the context of probabilistic trajectory prediction"
    - "rules of the road predicting driving behavior with a convolutional model of semantic interactions"

Connected through hyperedge HE-3 (induced by source paper "tppo a novel trajectory predictor with pseudo oracle"):
  with members:
    - "social and scene aware trajectory prediction in crowded spaces"
    - "scene induced multi modal trajectory forecasting via planning"
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "rules of the road predicting driving behavior with a convolutional model of semantic interactions"
    - "safecritic collision aware trajectory prediction"
    - "scene compliant trajectory forecast with agent centric spatio temporal grids"
    - "social bigat multimodal trajectory forecasting using bicycle gan and graph attention networks"
    - "starnet pedestrian trajectory prediction using deep neural network in star topology"

Connected through hyperedge HE-4 (induced by source paper "visionnet a drivable space based interactive motion prediction network for autonomous driving"):
  with members:
    - "video generation from single semantic label map"
    - "interaction aware kalman neural networks for trajectory prediction"
    - "infer intermediate representations for future prediction"
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "starnet pedestrian trajectory prediction using deep neural network in star topology"
    - "peeking into the future predicting future person activities and locations in videos"
    - "video to video synthesis"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"

Connected through hyperedge HE-5 (induced by source paper "scene compliant trajectory forecast with agent centric spatio temporal grids"):
  with members:
    - "scene induced multi modal trajectory forecasting via planning"
    - "human motion trajectory prediction a survey"
    - "predicting motion of vulnerable road users using high definition maps and efficient convnets"
    - "understanding pedestrian vehicle interactions with vehicle mounted vision an lstm model and empirical analysis"
    - "peeking into the future predicting future person activities and locations in videos"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "convolutional social pooling for vehicle trajectory prediction"

Connected through hyperedge HE-6 (induced by source paper "social bigat multimodal trajectory forecasting using bicycle gan and graph attention networks"):
  with members:
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "real world anomaly detection in surveillance videos"
    - "particle based pedestrian path prediction using lstm mdl models"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "context aware trajectory prediction"
    - "attention is all you need"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "tree memory networks for modelling long term temporal dependencies"

Connected through hyperedge HE-7 (induced by source paper "gamma a general agent motion prediction model for autonomous driving"):
  with members:
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "an lstm network for highway trajectory prediction"
    - "porca modeling and planning for autonomous driving among many pedestrians"

Connected through hyperedge HE-8 (induced by source paper "what the constant velocity model can teach us about pedestrian motion prediction"):
  with members:
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "a transferable pedestrian motion prediction model for intersections with different geometries"
    - "context aware pedestrian motion prediction in urban intersections"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "autonomous vehicles that interact with pedestrians a survey of theory and practice"
    - "context aware trajectory prediction"

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.CV(Computer Vision and Pattern Recognition)