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: "scene compliant trajectory forecast with agent centric spatio temporal grids"
  Abstract: Forecasting long-term human motion is a challenging task due to the non-linearity, multi-modality and inherent uncertainty in future trajectories. The underlying scene and past motion of agents can provide useful cues to predict their future motion. However, the heterogeneity of the two inputs poses a challenge for learning a joint representation of the scene and past trajectories. To address this challenge, we propose a model based on grid representations to forecast agent trajectories. We represent the past trajectories of agents using binary 2-D grids, and the underlying scene as a RGB birds-eye view (BEV) image, with an agent-centric frame of reference. We encode the scene and past trajectories using convolutional layers and generate trajectory forecasts using a Convolutional LSTM (ConvLSTM) decoder. Results on the publicly available Stanford Drone Dataset (SDD) show that our model outperforms prior approaches and outputs realistic future trajectories that comply with scene structure and past motion.

Connected through hyperedge HE-1 (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"
    - "social ways learning multi modal distributions of pedestrian trajectories with gans"
    - "diverse trajectory forecasting with determinantal point processes"
    - "multiple futures prediction"
    - "analyzing the variety loss in the context of probabilistic trajectory prediction"

Connected through hyperedge HE-2 (induced by source paper "transformer networks for trajectory forecasting"):
  with members:
    - "trajectory forecasts in unknown environments conditioned on grid based plans"
    - "trajectron multi agent generative trajectory forecasting with heterogeneous data for control"
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "human motion trajectory prediction a survey"
    - "safecritic collision aware trajectory prediction"
    - "generating long sequences with sparse transformers"
    - "social bigat multimodal trajectory forecasting using bicycle gan and graph attention networks"
    - "roberta a robustly optimized bert pretraining approach"

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"
    - "social ways learning multi modal distributions of pedestrian trajectories with gans"
    - "rules of the road predicting driving behavior with a convolutional model of semantic interactions"
    - "safecritic collision aware trajectory prediction"
    - "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 "trajectory forecasts in unknown environments conditioned on grid based plans"):
  with members:
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "conditional flow variational autoencoders for structured sequence prediction"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "social gan socially acceptable trajectories with generative adversarial networks"

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)