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: "particle based pedestrian path prediction using lstm mdl models"
  Abstract: Recurrent neural networks are able to learn complex long-term relationships from sequential data and output a pdf over the state space. Therefore, recurrent models are a natural choice to address path prediction tasks, where a trained model is used to generate future expectations from past observations. When applied to security applications, like predicting the path of pedestrians for risk assessment, a point-wise greedy (ML) evaluation of the output pdf is not feasible, since the environment often allows multiple choices. Therefore, a robust risk assessment has to take all options into account, even if they are overall not very likely. #R##N#Towards this end, a combination of particle filter sampling strategies and a LSTM-MDL model is proposed to address a multi-modal path prediction task. The capabilities and viability of the proposed approach are evaluated on several synthetic test conditions, yielding the counter-intuitive result that the simplest approach performs best. Further, the feasibility of the proposed approach is illustrated on several real world scenes.

Connected through hyperedge HE-1 (induced by source paper "social bigat multimodal trajectory forecasting using bicycle gan and graph attention networks"):
  with members:
    - "social ways learning multi modal distributions of pedestrian trajectories with gans"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "real world anomaly detection in surveillance videos"
    - "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-2 (induced by source paper "an rnn based imm filter surrogate"):
  with members:
    - "multi modal trajectory prediction of surrounding vehicles with maneuver based lstms"
    - "microscopic pedestrian flow characteristics development of an image processing data collection and simulation model"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "a recurrent latent variable model for sequential data"
    - "adam a method for stochastic optimization"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "speech recognition with deep recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "deep learning and statistical models for time critical pedestrian behaviour prediction"):
  with members:
    - "fused dnn a deep neural network fusion approach to fast and robust pedestrian detection"
    - "scale aware fast r cnn for pedestrian detection"
    - "learning complexity aware cascades for deep pedestrian detection"
    - "taking a deeper look at pedestrians"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "sophie an attentive gan for predicting paths compliant to social and physical constraints"):
  with members:
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "context aware trajectory prediction"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "tree memory networks for modelling long term temporal dependencies"
    - "soft hardwired attention an lstm framework for human trajectory prediction and abnormal event detection"
    - "car net clairvoyant attentive recurrent network"
    - "generating long term trajectories using deep hierarchical networks"
    - "probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network"

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)