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 for human robot interaction"
  Abstract: In this paper, we present an approach for robot learning of social affordance from human activity videos. We consider the problem in the context of human-robot interaction: Our approach learns structural representations of human-human (and human-object-human) interactions, describing how body-parts of each agent move with respect to each other and what spatial relations they should maintain to complete each sub-event (i.e., sub-goal). This enables the robot to infer its own movement in reaction to the human body motion, allowing it to naturally replicate such interactions. #R##N#We introduce the representation of social affordance and propose a generative model for its weakly supervised learning from human demonstration videos. Our approach discovers critical steps (i.e., latent sub-events) in an interaction and the typical motion associated with them, learning what body-parts should be involved and how. The experimental results demonstrate that our Markov Chain Monte Carlo (MCMC) based learning algorithm automatically discovers semantically meaningful interactive affordance from RGB-D videos, which allows us to generate appropriate full body motion for an agent.

Connected through hyperedge HE-1 (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"
    - "learning social affordance grammar from videos transferring human interactions to human robot interactions"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "it s all relative monocular 3d human pose estimation from weakly supervised data"):
  with members:
    - "lcr net multi person 2d and 3d pose detection in natural images"
    - "3d human pose estimation in the wild by adversarial learning"
    - "exploiting temporal information for 3d human pose estimation"
    - "benchmarking and error diagnosis in multi instance pose estimation"
    - "lifting from the deep convolutional 3d pose estimation from a single image"
    - "end to end recovery of human shape and pose"
    - "learning depth from monocular videos using direct methods"
    - "a simple yet effective baseline for 3d human pose estimation"

Connected through hyperedge HE-3 (induced by source paper "learning to act properly predicting and explaining affordances from images"):
  with members:
    - "joint discovery of object states and manipulation actions"
    - "situation recognition with graph neural networks"
    - "show adapt and tell adversarial training of cross domain image captioner"
    - "moviegraphs towards understanding human centric situations from videos"
    - "towards diverse and natural image descriptions via a conditional gan"
    - "semantic object parsing with graph lstm"
    - "gated graph sequence neural networks"
    - "generating visual explanations"

Connected through hyperedge HE-4 (induced by source paper "learning robot activities from first person human videos using convolutional future regression"):
  with members:
    - "learning dexterous manipulation for a soft robotic hand from human demonstration"
    - "unsupervised learning for physical interaction through video prediction"
    - "deep visual foresight for planning robot motion"
    - "ssd single shot multibox detector"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "end to end training of deep visuomotor policies"
    - "pooled motion features for first person videos"

Connected through hyperedge HE-5 (induced by source paper "learning social affordance grammar from videos transferring human interactions to human robot interactions"):
  with members:
    - "structure inference machines recurrent neural networks for analyzing relations in group activity recognition"
    - "action recognition by hierarchical mid level action elements"
    - "joint inference of groups events and human roles in aerial videos"

Connected through hyperedge HE-6 (induced by source paper "beyond holistic object recognition enriching image understanding with part states"):
  with members:
    - "recognizing car fluents from video"
    - "visual relationship detection with language priors"
    - "deep residual learning for image recognition"
    - "human pose estimation with iterative error feedback"
    - "learning deconvolution network for semantic segmentation"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "very deep convolutional networks for large scale image recognition"

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)