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: "intent aware multi agent reinforcement learning"
  Abstract: This paper proposes an intent-aware multi-agent planning framework as well as a learning algorithm. Under this framework, an agent plans in the goal space to maximize the expected utility. The planning process takes the belief of other agents' intents into consideration. Instead of formulating the learning problem as a partially observable Markov decision process (POMDP), we propose a simple but effective linear function approximation of the utility function. It is based on the observation that for humans, other people's intents will pose an influence on our utility for a goal. The proposed framework has several major advantages: i) it is computationally feasible and guaranteed to converge. ii) It can easily integrate existing intent prediction and low-level planning algorithms. iii) It does not suffer from sparse feedbacks in the action space. We experiment our algorithm in a real-world problem that is non-episodic, and the number of agents and goals can vary over time. Our algorithm is trained in a scene in which aerial robots and humans interact, and tested in a novel scene with a different environment. Experimental results show that our algorithm achieves the best performance and human-like behaviors emerge during the dynamic process.

Connected through hyperedge HE-1 (induced by source paper "cmetric a driving behavior measure using centrality functions"):
  with members:
    - "behavior planning of autonomous cars with social perception"
    - "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms"
    - "roadtrack tracking road agents in dense and heterogeneous environments"
    - "densepeds pedestrian tracking in dense crowds using front rvo and sparse features"
    - "graphrqi classifying driver behaviors using graph spectrums"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "identifying driver behaviors using trajectory features for vehicle navigation"

Connected through hyperedge HE-2 (induced by source paper "learning 3d aware egocentric spatial temporal interaction via graph convolutional networks"):
  with members:
    - "towards robust monocular depth estimation mixing datasets for zero shot cross dataset transfer"
    - "spatio temporal action graph networks"
    - "learning actor relation graphs for group activity recognition"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "classifying collisions with spatio temporal action graph networks"
    - "semi supervised learning fusion of self supervised supervised learning and multimodal cues for tactical driver behavior detection"
    - "graph r cnn for scene graph generation"

Connected through hyperedge HE-3 (induced by source paper "graphrqi classifying driver behaviors using graph spectrums"):
  with members:
    - "behavior planning of autonomous cars with social perception"
    - "self driving cars a survey"
    - "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms"
    - "roadtrack tracking road agents in dense and heterogeneous environments"
    - "rules of the road predicting driving behavior with a convolutional model of semantic interactions"
    - "densepeds pedestrian tracking in dense crowds using front rvo and sparse features"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"
    - "social gan socially acceptable trajectories with generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "human centric indoor scene synthesis using stochastic grammar"):
  with members:
    - "carla an open urban driving simulator"
    - "predicting human activities using stochastic grammar"
    - "cut paste and learn surprisingly easy synthesis for instance detection"
    - "unrealcv connecting computer vision to unreal engine"
    - "semantic scene completion from a single depth image"
    - "synthesizing training images for boosting human 3d pose estimation"
    - "clevr a diagnostic dataset for compositional language and elementary visual reasoning"
    - "deepcontext context encoding neural pathways for 3d holistic scene understanding"

Connected through hyperedge HE-5 (induced by source paper "motion planning among dynamic decision making agents with deep reinforcement learning"):
  with members:
    - "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"
    - "socially aware motion planning with deep reinforcement learning"
    - "role playing learning for socially concomitant mobile robot navigation"
    - "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation"
    - "crossmodal attentive skill learner"
    - "ssd single shot multibox detector"
    - "dynamic arrival rate estimation for campus mobility on demand network graphs"
    - "tensorflow a system for large scale machine learning"

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.AI(Artificial Intelligence)