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: "f formation detection individuating free standing conversational groups in images"
  Abstract: Detection of groups of interacting people is a very interesting and useful task in many modern technologies, with application fields spanning from video-surveillance to social robotics. In this paper we first furnish a rigorous definition of group considering the background of the social sciences: this allows us to specify many kinds of group, so far neglected in the Computer Vision literature. On top of this taxonomy we present a detailed state of the art on the group detection algorithms. Then, as a main contribution, we present a brand new method for the automatic detection of groups in still images, which is based on a graph-cuts framework for clustering individuals; in particular, we are able to codify in a computational sense the sociological definition of F-formation, that is very useful to encode a group having only proxemic information: position and orientation of people. We call the proposed method Graph-Cuts for F-formation (GCFF). We show how GCFF definitely outperforms all the state of the art methods in terms of different accuracy measures (some of them are brand new), demonstrating also a strong robustness to noise and versatility in recognizing groups of various cardinality.

Connected through hyperedge HE-1 (induced by source paper "towards social artificial intelligence nonverbal social signal prediction in a triadic interaction"):
  with members:
    - "on the convergence of adam and beyond"
    - "total capture a 3d deformation model for tracking faces hands and bodies"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "learning to generate long term future via hierarchical prediction"
    - "towards 3d human pose estimation in the wild a weakly supervised approach"
    - "recent trends in deep learning based natural language processing"
    - "a simple yet effective baseline for 3d human pose estimation"
    - "hand keypoint detection in single images using multiview bootstrapping"

Connected through hyperedge HE-2 (induced by source paper "mgpi a computational model of multiagent group perception and interaction"):
  with members:
    - "dante deep affinity network for clustering conversational interactants"
    - "counterfactual multi agent policy gradients"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "emergence of grounded compositional language in multi agent populations"
    - "coordinated multi agent imitation learning"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "learning multiagent communication with backpropagation"

Connected through hyperedge HE-3 (induced by source paper "modeling social group communication with multi agent imitation learning"):
  with members:
    - "counterfactual multi agent policy gradients"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "emergence of grounded compositional language in multi agent populations"
    - "coordinated multi agent imitation learning"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "learning to communicate with deep multi agent reinforcement learning"
    - "learning multiagent communication with backpropagation"
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-4 (induced by source paper "dante deep affinity network for clustering conversational interactants"):
  with members:
    - "social gan socially acceptable trajectories with generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "detecting f formations roles in crowded social scenes with wearables combining proxemics dynamics using lstms"):
  with members:
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "toward storytelling from visual lifelogging an overview"):
  with members:
    - "r clustering for egocentric video segmentation"
    - "semantic summarization of egocentric photo stream events"
    - "multi face tracking by extended bag of tracklets in egocentric photo streams"
    - "a survey on recent advances of computer vision algorithms for egocentric video"
    - "leveraging context to support automated food recognition in restaurants"
    - "predicting daily activities from egocentric images using deep learning"
    - "sequence to sequence video to text"
    - "egocentric field of view localization using first person point of view devices"

Connected through hyperedge HE-7 (induced by source paper "towards social pattern characterization in egocentric photo streams"):
  with members:
    - "all the people around me face discovery in egocentric photo streams"
    - "toward storytelling from visual lifelogging an overview"
    - "training deep networks for facial expression recognition with crowd sourced label distribution"
    - "multi face tracking by extended bag of tracklets in egocentric photo streams"
    - "with whom do i interact detecting social interactions in egocentric photo streams"
    - "very deep convolutional networks for large scale image recognition"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "clothing and people a social signal processing perspective"):
  with members:
    - "understanding neural networks through deep visualization"
    - "matching cnn meets knn quasi parametric human parsing"
    - "deep human parsing with active template regression"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "understanding deep image representations by inverting them"
    - "visualizing and understanding convolutional networks"

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.CV(Computer Vision and Pattern Recognition)