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: "generative modeling of multimodal multi human behavior"
  Abstract: This work presents a methodology for modeling and predicting human behavior in settings with N humans interacting in highly multimodal scenarios (i.e. where there are many possible highly-distinct futures). A motivating example includes robots interacting with humans in crowded environments, such as self-driving cars operating alongside human-driven vehicles or human-robot collaborative bin packing in a warehouse. Our approach to model human behavior in such uncertain environments is to model humans in the scene as nodes in a graphical model, with edges encoding relationships between them. For each human, we learn a multimodal probability distribution over future actions from a dataset of multi-human interactions. Learning such distributions is made possible by recent advances in the theory of conditional variational autoencoders and deep learning approximations of probabilistic graphical models. Specifically, we learn action distributions conditioned on interaction history, neighboring human behavior, and candidate future agent behavior in order to take into account response dynamics. We demonstrate the performance of such a modeling approach in modeling basketball player trajectories, a highly multimodal, multi-human scenario which serves as a proxy for many robotic applications.

Connected through hyperedge HE-1 (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 multi agent behavioral cloning"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"

Connected through hyperedge HE-2 (induced by source paper "rules of the road predicting driving behavior with a convolutional model of semantic interactions"):
  with members:
    - "human action recognition and prediction a survey"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "voxelnet end to end learning for point cloud based 3d object detection"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "long term on board prediction of people in traffic scenes under uncertainty"
    - "what uncertainties do we need in bayesian deep learning for computer vision"

Connected through hyperedge HE-3 (induced by source paper "multi agent reinforcement learning with multi step generative models"):
  with members:
    - "learning latent dynamics for planning from pixels"
    - "model ensemble trust region policy optimization"
    - "deep imitative models for flexible inference planning and control"
    - "unity a general platform for intelligent agents"
    - "learning latent subspaces in variational autoencoders"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "prediction and control with temporal segment models"
    - "emergence of grounded compositional language in multi agent populations"

Connected through hyperedge HE-4 (induced by source paper "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"):
  with members:
    - "rules of the road predicting driving behavior with a convolutional model of semantic interactions"
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "accurate and diverse sampling of sequences based on a best of many sample objective"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "carla an open urban driving simulator"

Connected through hyperedge HE-5 (induced by source paper "the trajectron probabilistic multi agent trajectory modeling with dynamic spatiotemporal graphs"):
  with members:
    - "graph networks as learnable physics engines for inference and control"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "stochastic video generation with a learned prior"
    - "relational inductive biases deep learning and graph networks"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "stochastic variational video prediction"
    - "fixing a broken elbo"
    - "massive exploration of neural machine translation architectures"

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)