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: "generating multi agent trajectories using programmatic weak supervision"
  Abstract: We study the problem of training sequential generative models for capturing coordinated multi-agent trajectory behavior, such as offensive basketball gameplay. When modeling such settings, it is often beneficial to design hierarchical models that can capture long-term coordination using intermediate variables. Furthermore, these intermediate variables should capture interesting high-level behavioral semantics in an interpretable and manipulatable way. We present a hierarchical framework that can effectively learn such sequential generative models. Our approach is inspired by recent work on leveraging programmatically produced weak labels, which we extend to the spatiotemporal regime. In addition to synthetic settings, we show how to instantiate our framework to effectively model complex interactions between basketball players and generate realistic multi-agent trajectories of basketball gameplay over long time periods. We validate our approach using both quantitative and qualitative evaluations, including a user study comparison conducted with professional sports analysts.

Connected through hyperedge HE-1 (induced by source paper "relational state space model for stochastic multi object systems"):
  with members:
    - "stochastic prediction of multi agent interactions from partial observations"
    - "graph normalizing flows"
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "social bigat multimodal trajectory forecasting using bicycle gan and graph attention networks"
    - "emerging convolutions for generative normalizing flows"
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "state space lstm models with particle mcmc inference"

Connected through hyperedge HE-2 (induced by source paper "stochastic prediction of multi agent interactions from partial observations"):
  with members:
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "graph networks as learnable physics engines for inference and control"
    - "stochastic adversarial video prediction"
    - "generative multi agent behavioral cloning"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "stochastic video generation with a learned prior"
    - "learning and querying fast generative models for reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "unsupervised learning of object structure and dynamics from videos"):
  with members:
    - "learning character agnostic motion for motion retargeting in 2d"
    - "unsupervised learning of object keypoints for perception and control"
    - "stochastic prediction of multi agent interactions from partial observations"
    - "unsupervised discovery of parts structure and dynamics"
    - "video to video synthesis"
    - "hierarchical long term video prediction without supervision"
    - "deepmind control suite"
    - "learning latent dynamics for planning from pixels"

Connected through hyperedge HE-4 (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"
    - "self consistent trajectory autoencoder hierarchical reinforcement learning with trajectory embeddings"
    - "toward controlled generation of text"

Connected through hyperedge HE-5 (induced by source paper "on multi agent learning in team sports games"):
  with members:
    - "winning isn t everything training human like agents for playtesting and game ai"
    - "skynet a top deep rl agent in the inaugural pommerman team competition"
    - "safer deep rl with shallow mcts a case study in pommerman"
    - "learning latent plans from play"
    - "a survey and critique of multiagent deep reinforcement learning"
    - "multi agent adversarial inverse reinforcement learning"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "learning dexterous in hand manipulation"

Connected through hyperedge HE-6 (induced by source paper "precog prediction conditioned on goals in visual multi agent settings"):
  with members:
    - "stochastic prediction of multi agent interactions from partial observations"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "generative hybrid representations for activity forecasting with no regret learning"
    - "an algorithmic perspective on imitation learning"
    - "deep imitative models for flexible inference planning and control"
    - "generative modeling of multimodal multi human behavior"
    - "generative multi agent behavioral cloning"

Connected through hyperedge HE-7 (induced by source paper "naomi non autoregressive multiresolution sequence imputation"):
  with members:
    - "gain missing data imputation using generative adversarial nets"
    - "brits bidirectional recurrent imputation for time series"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "end to end non autoregressive neural machine translation with connectionist temporal classification"
    - "progressive growing of gans for improved quality stability and variation"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "non autoregressive neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "imitation learning of factored multi agent reactive models"):
  with members:
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "fixing a broken elbo"
    - "generative models of visually grounded imagination"
    - "generative temporal models with memory"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "variational inference for monte carlo objectives"
    - "unsupervised perceptual rewards for imitation 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)