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: "online vehicle trajectory prediction using policy anticipation network and optimization based context reasoning"
  Abstract: In this paper, we present an online two-level vehicle trajectory prediction framework for urban autonomous driving where there are complex contextual factors, such as lane geometries, road constructions, traffic regulations and moving agents. Our method combines high-level policy anticipation with low-level context reasoning. We leverage a long short-term memory (LSTM) network to anticipate the vehicle's driving policy (e.g., forward, yield, turn left, turn right, etc.) using its sequential history observations. The policy is then used to guide a low-level optimization-based context reasoning process. We show that it is essential to incorporate the prior policy anticipation due to the multimodal nature of the future trajectory. Moreover, contrary to existing regression-based trajectory prediction methods, our optimization-based reasoning process can cope with complex contextual factors. The final output of the two-level reasoning process is a continuous trajectory that automatically adapts to different traffic configurations and accurately predicts future vehicle motions. The performance of the proposed framework is analyzed and validated in an emerging autonomous driving simulation platform (CARLA).

Connected through hyperedge HE-1 (induced by source paper "formulazero distributionally robust online adaptation via offline population synthesis"):
  with members:
    - "dota 2 with large scale deep reinforcement learning"
    - "adversarial policies attacking deep reinforcement learning"
    - "alphastar an evolutionary computation perspective"
    - "online control with adversarial disturbances"
    - "efficient black box assessment of autonomous vehicle safety"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "jointly learnable behavior and trajectory planning for self driving vehicles"
    - "regret bounds for robust adaptive control of the linear quadratic regulator"

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"
    - "interpolation in generative models"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "predicting vehicle behaviors over an extended horizon using behavior interaction network"
    - "interaction aware multi agent tracking and probabilistic behavior prediction via adversarial learning"
    - "interpreting the latent space of gans for semantic face editing"

Connected through hyperedge HE-3 (induced by source paper "deep learning based vehicle behaviour prediction for autonomous driving applications a review"):
  with members:
    - "graph neural networks for modelling traffic participant interaction"
    - "grip graph based interaction aware trajectory prediction"
    - "human motion trajectory prediction a survey"
    - "interaction aware kalman neural networks for trajectory prediction"
    - "predicting vehicle behaviors over an extended horizon using behavior interaction network"
    - "anomaly detection in road traffic using visual surveillance a survey"
    - "survey on vision based path prediction"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "predicting vehicle behaviors over an extended horizon using behavior interaction network"):
  with members:
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "understanding human behaviors in crowds by imitating the decision making process"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "probabilistic prediction of vehicle semantic intention and motion"
    - "context aware trajectory prediction"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "on a formal model of safe and scalable self driving cars"

Connected through hyperedge HE-5 (induced by source paper "safe trajectory generation for complex urban environments using spatio temporal semantic corridor"):
  with members:
    - "predicting vehicle behaviors over an extended horizon using behavior interaction network"
    - "search based optimal motion planning for automated driving"
    - "baidu apollo em motion planner"
    - "a convex optimization approach to smooth trajectories for motion planning with car like robots"

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)