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: "anticipating many futures online human motion prediction and synthesis for human robot collaboration"
  Abstract: Fluent and safe interactions of humans and robots require both partners to anticipate the others' actions. A common approach to human intention inference is to model specific trajectories towards known goals with supervised classifiers. However, these approaches do not take possible future movements into account nor do they make use of kinematic cues, such as legible and predictable motion. The bottleneck of these methods is the lack of an accurate model of general human motion. In this work, we present a conditional variational autoencoder that is trained to predict a window of future human motion given a window of past frames. Using skeletal data obtained from RGB depth images, we show how this unsupervised approach can be used for online motion prediction for up to 1660 ms. Additionally, we demonstrate online target prediction within the first 300-500 ms after motion onset without the use of target specific training data. The advantage of our probabilistic approach is the possibility to draw samples of possible future motions. Finally, we investigate how movements and kinematic cues are represented on the learned low dimensional manifold.

Connected through hyperedge HE-1 (induced by source paper "learning human motion models for long term predictions"):
  with members:
    - "deep representation learning for human motion prediction and classification"
    - "on human motion prediction using recurrent neural networks"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "spatio temporal lstm with trust gates for 3d human action recognition"
    - "structured prediction of 3d human pose with deep neural networks"
    - "space time representation of people based on 3d skeletal data a review"
    - "differential recurrent neural networks for action recognition"
    - "show and tell a neural image caption generator"

Connected through hyperedge HE-2 (induced by source paper "human robot collaboration from psychology to social robotics"):
  with members:
    - "deep representation learning for human motion prediction and classification"
    - "deep predictive policy training using reinforcement learning"
    - "self learning and adaptation in a sensorimotor framework"
    - "a sensorimotor reinforcement learning framework for physical human robot interaction"
    - "efficient model learning from joint action demonstrations for human robot collaborative tasks"
    - "end to end training of deep visuomotor policies"
    - "learning human activities and object affordances from rgb d videos"
    - "interaction histories and short term memory enactive development of turn taking behaviors in a childlike humanoid robot"

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)