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: "modeling cooperative navigation in dense human crowds"
  Abstract: For robots to be a part of our daily life, they need to be able to navigate among crowds not only safely but also in a socially compliant fashion. This is a challenging problem because humans tend to navigate by implicitly cooperating with one another to avoid collisions, while heading toward their respective destinations. Previous approaches have used hand-crafted functions based on proximity to model human-human and human-robot interactions. However, these approaches can only model simple interactions and fail to generalize for complex crowded settings. In this paper, we develop an approach that models the joint distribution over future trajectories of all interacting agents in the crowd, through a local interaction model that we train using real human trajectory data. The interaction model infers the velocity of each agent based on the spatial orientation of other agents in his vicinity. During prediction, our approach infers the goal of the agent from its past trajectory and uses the learned model to predict its future trajectory. We demonstrate the performance of our method against a state-of-the-art approach on a public dataset and show that our model outperforms when predicting future trajectories for longer horizons.

Connected through hyperedge HE-1 (induced by source paper "socially aware robot planning via bandit human feedback"):
  with members:
    - "socially aware motion planning with deep reinforcement learning"
    - "learning preferences for manipulation tasks from online coactive feedback"
    - "online optimization in dynamic environments improved regret rates for strongly convex problems"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-2 (induced by source paper "planning and execution using inaccurate models with provable guarantees"):
  with members:
    - "when to trust your model model based policy optimization"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "deep context map agent trajectory prediction using location specific latent maps"):
  with members:
    - "social bigat multimodal trajectory forecasting using bicycle gan and graph attention networks"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "multimodal interaction aware motion prediction for autonomous street crossing"
    - "context aware pedestrian motion prediction in urban intersections"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "car net clairvoyant attentive recurrent network"
    - "pedestrian prediction by planning using deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "robot navigation in crowds by graph convolutional networks with attention learned from human gaze"):
  with members:
    - "gaze training by modulated dropout improves imitation learning"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "agil learning attention from human for visuomotor tasks"
    - "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "fastgcn fast learning with graph convolutional networks via importance sampling"
    - "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "learning to communicate implicitly by actions"):
  with members:
    - "learning reciprocity in complex sequential social dilemmas"
    - "probabilistic recursive reasoning for multi agent reinforcement learning"
    - "bayesian action decoder for deep multi agent reinforcement learning"
    - "modeling others using oneself in multi agent reinforcement learning"
    - "learning to share and hide intentions using information regularization"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "machine theory of mind"

Connected through hyperedge HE-6 (induced by source paper "an evaluation of trajectory prediction approaches and notes on the trajnet benchmark"):
  with members:
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "mx lstm mixing tracklets and vislets to jointly forecast trajectories and head poses"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "on human motion prediction using recurrent neural networks"
    - "deep learning driven visual path prediction from a single image"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "crowdmove autonomous mapless navigation in crowded scenarios"):
  with members:
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "sociosense robot navigation amongst pedestrians with social and psychological constraints"
    - "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"
    - "from perception to decision a data driven approach to end to end motion planning for autonomous ground robots"
    - "socially aware motion planning with deep reinforcement learning"
    - "uncertainty aware reinforcement learning for collision avoidance"
    - "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-8 (induced by source paper "dynamic objects segmentation for visual localization in urban environments"):
  with members:
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "dynaslam tracking mapping and inpainting in dynamic scenes"
    - "driven to distraction self supervised distractor learning for robust monocular visual odometry in urban environments"
    - "carla an open urban driving simulator"
    - "a data driven model for interaction aware pedestrian motion prediction in object cluttered environments"
    - "mask r cnn"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "fast r cnn"

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)