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: "socially compliant navigation through raw depth inputs with generative adversarial imitation learning"
  Abstract: We present an approach for mobile robots to learn to navigate in dynamic environments with pedestrians via raw depth inputs, in a socially compliant manner. To achieve this, we adopt a generative adversarial imitation learning (GAIL) strategy, which improves upon a pre-trained behavior cloning policy. Our approach overcomes the disadvantages of previous methods, as they heavily depend on the full knowledge of the location and velocity information of nearby pedestrians, which not only requires specific sensors, but also the extraction of such state information from raw sensory input could consume much computation time. In this paper, our proposed GAIL-based model performs directly on raw depth inputs and plans in real-time. Experiments show that our GAIL-based approach greatly improves the safety and efficiency of the behavior of mobile robots from pure behavior cloning. The real-world deployment also shows that our method is capable of guiding autonomous vehicles to navigate in a socially compliant manner directly through raw depth inputs. In addition, we release a simulation plugin for modeling pedestrian behaviors based on the social force model.

Connected through hyperedge HE-1 (induced by source paper "social navigation with human empowerment driven reinforcement learning"):
  with members:
    - "deep local trajectory replanning and control for robot navigation"
    - "a survey on intrinsic motivation in reinforcement learning"
    - "exploring applications of deep reinforcement learning for real world autonomous driving systems"
    - "combining optimal control and learning for visual navigation in novel environments"
    - "social influence as intrinsic motivation for multi agent deep reinforcement learning"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "emi exploration with mutual information"
    - "intention net integrating planning and deep learning for goal directed autonomous navigation"

Connected through hyperedge HE-2 (induced by source paper "imitation learning for human pose prediction"):
  with members:
    - "procedure planning in instructional videos"
    - "peeking into the future predicting future person activities and locations in videos"
    - "mt vae learning motion transformations to generate multimodal human dynamics"
    - "action agnostic human pose forecasting"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "learning plannable representations with causal infogan"
    - "predicting deeper into the future of semantic segmentation"
    - "hp gan probabilistic 3d human motion prediction via gan"

Connected through hyperedge HE-3 (induced by source paper "adaptive curriculum generation from demonstrations for sim to real visuomotor control"):
  with members:
    - "learning to singulate objects using a push proposal network"
    - "solving rubik s cube with a robot hand"
    - "combining learned skills and reinforcement learning for robotic manipulations"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "playing hard exploration games by watching youtube"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "closing the sim to real loop adapting simulation randomization with real world experience"

Connected through hyperedge HE-4 (induced by source paper "learning to navigate from simulation via spatial and semantic information synthesis"):
  with members:
    - "yolov3 an incremental improvement"
    - "conditional affordance learning for driving in urban environments"
    - "virtual to real learning to control in visual semantic segmentation"
    - "navigation by imitation in a pedestrian rich environment"
    - "driving policy transfer via modularity and abstraction"
    - "a data efficient framework for training and sim to real transfer of navigation policies"
    - "visual representations for semantic target driven navigation"
    - "virtual to real reinforcement learning for autonomous driving"

Connected through hyperedge HE-5 (induced by source paper "deep local trajectory replanning and control for robot navigation"):
  with members:
    - "a deep learning based behavioral approach to indoor autonomous navigation"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "driving policy transfer via modularity and abstraction"
    - "mutan multimodal tucker fusion for visual question answering"
    - "simple online and realtime tracking with a deep association metric"
    - "social attention modeling attention in human crowds"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "visual based autonomous driving deployment from a stochastic and uncertainty aware perspective"):
  with members:
    - "model predictive policy learning with uncertainty regularization for driving in dense traffic"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "real to virtual domain unification for end to end autonomous driving"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "multimodal unsupervised image to image translation"
    - "safe reinforcement learning with model uncertainty estimates"
    - "drit diverse image to image translation via disentangled representations"
    - "virtual to real reinforcement learning for autonomous driving"

Connected through hyperedge HE-7 (induced by source paper "mapless navigation among dynamics with social safety awareness a reinforcement learning approach from 2d laser scans"):
  with members:
    - "a behavioral approach to visual navigation with graph localization networks"
    - "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning"
    - "getting robots unfrozen and unlost in dense pedestrian crowds"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "curiosity driven exploration for mapless navigation with deep reinforcement learning"
    - "crowdmove autonomous mapless navigation in crowded scenarios"
    - "intention net integrating planning and deep learning for goal directed autonomous navigation"
    - "towards optimally decentralized multi robot collision avoidance via deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "effects of a social force model reward in robot navigation based on deep reinforcement learning"):
  with members:
    - "situation aware pedestrian trajectory prediction with spatio temporal attention model"
    - "long range indoor navigation with prm rl"
    - "reward shaping via meta learning"
    - "deep visual mpc policy learning for navigation"
    - "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"
    - "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning"
    - "social gan socially acceptable trajectories with generative adversarial networks"

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)