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: "learning social affordance grammar from videos transferring human interactions to human robot interactions"
  Abstract: In this paper, we present a general framework for learning social affordance grammar as a spatiotemporal AND-OR graph (ST-AOG) from RGB-D videos of human interactions, and transfer the grammar to humanoids to enable a real-time motion inference for human-robot interaction (HRI). Based on Gibbs sampling, our weakly supervised grammar learning can automatically construct a hierarchical representation of an interaction with long-term joint sub-tasks of both agents and short term atomic actions of individual agents. Based on a new RGB-D video dataset with rich instances of human interactions, our experiments of Baxter simulation, human evaluation, and real Baxter test demonstrate that the model learned from limited training data successfully generates human-like behaviors in unseen scenarios and outperforms both baselines.

Connected through hyperedge HE-1 (induced by source paper "vrgym a virtual testbed for physical and interactive ai"):
  with members:
    - "configurable 3d scene synthesis and 2d image rendering with per pixel ground truth using stochastic grammars"
    - "gibson env real world perception for embodied agents"
    - "human centric indoor scene synthesis using stochastic grammar"
    - "predicting human activities using stochastic grammar"
    - "airsim high fidelity visual and physical simulation for autonomous vehicles"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "ai2 thor an interactive 3d environment for visual ai"
    - "semantic scene completion from a single depth image"

Connected through hyperedge HE-2 (induced by source paper "robot learning and execution of collaborative manipulation plans from youtube videos"):
  with members:
    - "procedure planning in instructional videos"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "recipe1m a dataset for learning cross modal embeddings for cooking recipes and food images"
    - "unsupervised learning and segmentation of complex activities from video"
    - "yolov3 an incremental improvement"
    - "functional object oriented network construction expansion"
    - "translating videos to commands for robotic manipulation with deep recurrent neural networks"
    - "towards automatic learning of procedures from web instructional videos"

Connected through hyperedge HE-3 (induced by source paper "demonstration guided deep reinforcement learning of control policies for dexterous human robot interaction"):
  with members:
    - "learning dexterous in hand manipulation"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"
    - "learning human behaviors from motion capture by adversarial imitation"
    - "proximal policy optimization algorithms"
    - "generative adversarial imitation learning"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"

Connected through hyperedge HE-4 (induced by source paper "visual affordance and function understanding a survey"):
  with members:
    - "binge watching scaling affordance learning from sitcoms"
    - "geometric affordances from a single example via the interaction tensor"
    - "predicting human activities using stochastic grammar"
    - "deep affordance grounded sensorimotor object recognition"
    - "transfer of tool affordance and manipulation cues with 3d vision data"
    - "adaptive binarization for weakly supervised affordance segmentation"
    - "learning to act properly predicting and explaining affordances from images"
    - "attribute recognition by joint recurrent learning of context and correlation"

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)