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: "coordinated multi agent imitation learning"
  Abstract: We study the problem of imitation learning from demonstrations of multiple coordinating agents. One key challenge in this setting is that learning a good model of coordination can be difficult, since coordination is often implicit in the demonstrations and must be inferred as a latent variable. We propose a joint approach that simultaneously learns a latent coordination model along with the individual policies. In particular, our method integrates unsupervised structure learning with conventional imitation learning. We illustrate the power of our approach on a difficult problem of learning multiple policies for fine-grained behavior modeling in team sports, where different players occupy different roles in the coordinated team strategy. We show that having a coordination model to infer the roles of players yields substantially improved imitation loss compared to conventional baselines.

Connected through hyperedge HE-1 (induced by source paper "multi agent interactions modeling with correlated policies"):
  with members:
    - "a regularized opponent model with maximum entropy objective"
    - "probabilistic recursive reasoning for multi agent reinforcement learning"
    - "multi agent deep reinforcement learning for large scale traffic signal control"
    - "multi agent adversarial inverse reinforcement learning"
    - "autonomous agents modelling other agents a comprehensive survey and open problems"
    - "learning policy representations in multiagent systems"
    - "multi agent generative adversarial imitation learning"
    - "multi agent inverse reinforcement learning for general sum stochastic games"

Connected through hyperedge HE-2 (induced by source paper "scalable multi agent inverse reinforcement learning via actor attention critic"):
  with members:
    - "modeling social group communication with multi agent imitation learning"
    - "multi agent adversarial inverse reinforcement learning"
    - "pytorch an imperative style high performance deep learning library"
    - "actor attention critic for multi agent reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "multi agent generative adversarial imitation learning"
    - "discriminator actor critic addressing sample inefficiency and reward bias in adversarial imitation learning"
    - "counterfactual multi agent policy gradients"

Connected through hyperedge HE-3 (induced by source paper "learning hierarchical teaching policies for cooperative agents"):
  with members:
    - "teaching on a budget in multi agent deep reinforcement learning"
    - "probabilistic recursive reasoning for multi agent reinforcement learning"
    - "learning abstract options"
    - "learning to teach in cooperative multiagent reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "hierarchical imitation and reinforcement learning"
    - "automated curriculum learning for neural networks"
    - "counterfactual multi agent policy gradients"

Connected through hyperedge HE-4 (induced by source paper "learning self correctable policies and value functions from demonstrations with negative sampling"):
  with members:
    - "generative predecessor models for sample efficient imitation learning"
    - "reinforcement learning from imperfect demonstrations"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "an algorithmic perspective on imitation learning"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "dual policy iteration"
    - "model ensemble trust region policy optimization"
    - "off policy deep reinforcement learning without exploration"

Connected through hyperedge HE-5 (induced by source paper "crowd density forecasting by modeling patch based dynamics"):
  with members:
    - "locality constrained spatial transformer network for video crowd counting"
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "human motion trajectory prediction a survey"
    - "looking to relations for future trajectory forecast"
    - "c 3 framework an open source pytorch code for crowd counting"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "discrete residual flow for probabilistic pedestrian behavior prediction"
    - "precog prediction conditioned on goals in visual multi agent settings"

Connected through hyperedge HE-6 (induced by source paper "learning calibratable policies using programmatic style consistency"):
  with members:
    - "deepmind control suite"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"
    - "adversarial information factorization"
    - "isolating sources of disentanglement in variational autoencoders"
    - "learning latent subspaces in variational autoencoders"
    - "generating multi agent trajectories using programmatic weak supervision"
    - "self consistent trajectory autoencoder hierarchical reinforcement learning with trajectory embeddings"

Connected through hyperedge HE-7 (induced by source paper "state alignment based imitation learning"):
  with members:
    - "wasserstein adversarial imitation learning"
    - "provably efficient imitation learning from observation alone"
    - "efficient exploration via state marginal matching"
    - "imitation learning as f divergence minimization"
    - "imitating latent policies from observation"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "playing hard exploration games by watching youtube"
    - "adversarial imitation via variational inverse reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "hierarchical cooperative multi agent reinforcement learning with skill discovery"):
  with members:
    - "integrating independent and centralized multi agent reinforcement learning for traffic signal network optimization"
    - "on multi agent learning in team sports games"
    - "winning isn t everything training human like agents for playtesting and game ai"
    - "feudal multi agent hierarchies for cooperative reinforcement learning"
    - "google research football a novel reinforcement learning environment"
    - "qtran learning to factorize with transformation for cooperative multi agent reinforcement learning"
    - "emergent coordination through competition"
    - "a survey and critique of multiagent 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)