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: "multimodal probabilistic model based planning for human robot interaction"
  Abstract: This paper presents a method for constructing human-robot interaction policies in settings where multimodality, i.e., the possibility of multiple highly distinct futures, plays a critical role in decision making. We are motivated in this work by the example of traffic weaving, e.g., at highway on-ramps/off-ramps, where entering and exiting cars must swap lanes in a short distance---a challenging negotiation even for experienced drivers due to the inherent multimodal uncertainty of who will pass whom. Our approach is to learn multimodal probability distributions over future human actions from a dataset of human-human exemplars and perform real-time robot policy construction in the resulting environment model through massively parallel sampling of human responses to candidate robot action sequences. Direct learning of these distributions is made possible by recent advances in the theory of conditional variational autoencoders (CVAEs), whereby we learn action distributions simultaneously conditioned on the present interaction history, as well as candidate future robot actions in order to take into account response dynamics. We demonstrate the efficacy of this approach with a human-in-the-loop simulation of a traffic weaving scenario.

Connected through hyperedge HE-1 (induced by source paper "inference based strategy alignment for general sum differential games"):
  with members:
    - "on gradient based learning in continuous games"
    - "cooperation aware reinforcement learning for merging in dense traffic"
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "on the convergence of gradient based learning in continuous games"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "hierarchical game theoretic planning for autonomous vehicles"
    - "a scalable framework for real time multi robot multi human collision avoidance"
    - "the value of inferring the internal state of traffic participants for autonomous freeway driving"

Connected through hyperedge HE-2 (induced by source paper "planning under non rational perception of uncertain spatial costs"):
  with members:
    - "gesture based human swarm interactions for formation control using interpreters"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-3 (induced by source paper "learning representations from imperfect time series data via tensor rank regularization"):
  with members:
    - "multimodal language analysis with recurrent multistage fusion"
    - "low rank tensor completion for multiway visual data"
    - "multimodal affective analysis using hierarchical attention strategy with word level alignment"
    - "found in translation learning robust joint representations by cyclic translations between modalities"
    - "tensor regression networks"
    - "tensor fusion network for multimodal sentiment analysis"
    - "weighted low rank tensor recovery for hyperspectral image restoration"
    - "multimodal compact bilinear pooling for multimodal neural machine translation"

Connected through hyperedge HE-4 (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"
    - "generative modeling of multimodal multi human behavior"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "on the utility of model learning in hri"):
  with members:
    - "shared autonomy via deep reinforcement learning"
    - "least squares temporal difference learning for the linear quadratic regulator"
    - "proximal policy optimization algorithms"
    - "unsupervised learning for physical interaction through video prediction"
    - "trust region policy optimization"
    - "shared autonomy via hindsight optimization"
    - "adam a method for stochastic optimization"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "starnet pedestrian trajectory prediction using deep neural network in star topology"):
  with members:
    - "porca modeling and planning for autonomous driving among many pedestrians"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "soft hardwired attention an lstm framework for human trajectory prediction and abnormal event detection"
    - "social attention modeling attention in human crowds"
    - "human trajectory prediction using spatially aware deep attention models"
    - "a neural representation of sketch drawings"
    - "end to end attention based large vocabulary speech recognition"
    - "on the properties of neural machine translation encoder decoder approaches"

Connected through hyperedge HE-7 (induced by source paper "a hamilton jacobi reachability based framework for predicting and analyzing human motion for safe planning"):
  with members:
    - "human motion trajectory prediction a survey"
    - "combining optimal control and learning for visual navigation in novel environments"
    - "safe motion planning in unknown environments optimality benchmarks and tractable policies"
    - "a general safety framework for learning based control in uncertain robotic systems"
    - "hamilton jacobi reachability a brief overview and recent advances"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "forecasting interactive dynamics of pedestrians with fictitious play"
    - "finding approximate pomdp solutions through belief compression"

Connected through hyperedge HE-8 (induced by source paper "cooperation aware reinforcement learning for merging in dense traffic"):
  with members:
    - "interaction aware decision making with adaptive strategies under merging scenarios"
    - "safe reinforcement learning with scene decomposition for navigating complex urban environments"
    - "collaborative planning for mixed autonomy lane merging"
    - "hierarchical game theoretic planning for autonomous vehicles"
    - "prioritized experience replay"

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)