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: "learning from demonstration in the wild"
  Abstract: Learning from demonstration (LfD) is useful in settings where hand-coding behaviour or a reward function is impractical. It has succeeded in a wide range of problems but typically relies on manually generated demonstrations or specially deployed sensors and has not generally been able to leverage the copious demonstrations available in the wild: those that capture behaviours that were occurring anyway using sensors that were already deployed for another purpose, e.g., traffic camera footage capturing demonstrations of natural behaviour of vehicles, cyclists, and pedestrians. We propose Video to Behaviour (ViBe), a new approach to learn models of behaviour from unlabelled raw video data of a traffic scene collected from a single, monocular, initially uncalibrated camera with ordinary resolution. Our approach calibrates the camera, detects relevant objects, tracks them through time, and uses the resulting trajectories to perform LfD, yielding models of naturalistic behaviour. We apply ViBe to raw videos of a traffic intersection and show that it can learn purely from videos, without additional expert knowledge.

Connected through hyperedge HE-1 (induced by source paper "plop probabilistic polynomial objects trajectory planning for autonomous driving"):
  with members:
    - "trajectory forecasts in unknown environments conditioned on grid based plans"
    - "urban driving with conditional imitation learning"
    - "grip graph based interaction aware trajectory prediction"
    - "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"
    - "multiple futures prediction"
    - "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms"
    - "conditional vehicle trajectories prediction in carla urban environment"
    - "on the variance of the adaptive learning rate and beyond"

Connected through hyperedge HE-2 (induced by source paper "independent generative adversarial self imitation learning in cooperative multiagent systems"):
  with members:
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "self imitation learning"
    - "multi agent generative adversarial imitation learning"
    - "towards cooperation in sequential prisoner s dilemmas a deep multiagent reinforcement learning approach"
    - "multi agent imitation learning for driving simulation"
    - "discriminator actor critic addressing sample inefficiency and reward bias in adversarial imitation learning"
    - "counterfactual multi agent policy gradients"
    - "guided deep reinforcement learning for swarm systems"

Connected through hyperedge HE-3 (induced by source paper "learning driving decisions by imitating drivers control behaviors"):
  with members:
    - "combining optimal control and learning for visual navigation in novel environments"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "deep imitative models for flexible inference planning and control"
    - "adversarial attacks on neural network policies"
    - "carla an open urban driving simulator"
    - "imitating driver behavior with generative adversarial networks"
    - "a general safety framework for learning based control in uncertain robotic systems"
    - "on a formal model of safe and scalable self driving cars"

Connected through hyperedge HE-4 (induced by source paper "an agency directed approach to test generation for simulation based autonomous vehicle verification"):
  with members:
    - "simulation based adversarial test generation for autonomous vehicles with machine learning components"
    - "scenarios for development test and validation of automated vehicles"
    - "ontology based scene creation for the development of automated vehicles"
    - "intelligent agent based stimulation for testing robotic software in human robot interactions"
    - "coverage driven verification an approach to verify code for robots that directly interact with humans"

Connected through hyperedge HE-5 (induced by source paper "backplay man muss immer umkehren"):
  with members:
    - "reinforcement learning from imperfect demonstrations"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "playing hard exploration games by watching youtube"
    - "forward backward reinforcement learning"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "barc backward reachability curriculum for robotic reinforcement learning"
    - "pommerman a multi agent playground"
    - "kickstarting deep reinforcement 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.LG(Machine Learning)