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: "drogon a causal reasoning framework for future trajectory forecast"
  Abstract: We propose DROGON (Deep RObust Goal-Oriented trajectory prediction Network) for accurate vehicle trajectory forecast by considering behavioral intention of vehicles in traffic scenes. Our main insight is that a causal relationship between intention and behavior of drivers can be reasoned from the observation of their relational interactions toward an environment. To succeed in causal reasoning, we build a conditional prediction model to forecast goal-oriented trajectories, which is trained with the following stages: (i) relational inference where we encode relational interactions of vehicles using the perceptual context; (ii) intention estimation to compute the probability distribution of intentional goals based on the inferred relations; and (iii) causal reasoning where we reason about the behavior of vehicles as future locations conditioned on the intention. To properly evaluate the performance of our approach, we present a new large-scale dataset collected at road intersections with diverse interactions of vehicles. The experiments demonstrate the efficacy of DROGON as it consistently outperforms state-of-the-art techniques.

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

Connected through hyperedge HE-2 (induced by source paper "nemo future object localization using noisy ego priors"):
  with members:
    - "looking to relations for future trajectory forecast"
    - "the h3d dataset for full surround 3d multi object detection and tracking in crowded urban scenes"
    - "what the constant velocity model can teach us about pedestrian motion prediction"
    - "egocentric vision based future vehicle localization for intelligent driving assistance systems"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "convolutional neural network for trajectory prediction"
    - "joint person segmentation and identification in synchronized first and third person videos"
    - "identifying first person camera wearers in third person videos"

Connected through hyperedge HE-3 (induced by source paper "cooperation aware lane change maneuver in dense traffic based on model predictive control with recurrent neural network"):
  with members:
    - "interaction aware decision making with adaptive strategies under merging scenarios"
    - "cooperation aware reinforcement learning for merging in dense traffic"
    - "real time ecological velocity planning for plug in hybrid vehicles with partial communication to traffic lights"
    - "safe reinforcement learning on autonomous vehicles"
    - "driving in dense traffic with model free reinforcement learning"
    - "design and implementation of ecological adaptive cruise control for autonomous driving with communication to traffic lights"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "explaining how a deep neural network trained with end to end learning steers a car"

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)