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: "gamma a general agent motion prediction model for autonomous driving"
  Abstract: Autonomous driving in mixed traffic requires reliable motion prediction of nearby traffic agents such as pedestrians, bicycles, cars, buses, etc.. This prediction problem is extremely challenging because of the diverse dynamics and geometry of traffic agents, complex road conditions, and intensive interactions among the agents. In this paper, we proposed GAMMA, a general agent motion prediction model for autonomous driving, that can predict the motion of heterogeneous traffic agents with different kinematics, geometry, human agents' inner states, etc.. GAMMA formalizes motion prediction as geometric optimization in the velocity space, and integrates physical constraints and human inner states into this unified framework. Our results show that GAMMA outperforms state-of-the-art approaches significantly on diverse real-world datasets.

Connected through hyperedge HE-1 (induced by source paper "summit a simulator for urban driving in massive mixed traffic"):
  with members:
    - "lets drive driving in a crowd by learning from tree search"
    - "hyp despot a hybrid parallel algorithm for online planning under uncertainty"
    - "learning dexterous in hand manipulation"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "porca modeling and planning for autonomous driving among many pedestrians"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "carla an open urban driving simulator"
    - "playing for benchmarks"

Connected through hyperedge HE-2 (induced by source paper "estimating the potential for shared autonomous scooters"):
  with members:
    - "porca modeling and planning for autonomous driving among many pedestrians"
    - "on the interaction between autonomous mobility on demand and public transportation systems"

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)