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: "towards an integrated approach to crowd analysis and crowd synthesis"
  Abstract: An adaptive model for the simulation of groups of pedestrians able to preserve their cohesion.An observation and analysis of groups of pedestrians in a real world situation.An integrated framework for crowd analysis and synthesis. Studies related to crowds of pedestrians, both those of theoretical nature and application oriented ones, have generally focused on either the analysis or the synthesis of the phenomena related to the interplay between individual pedestrians, each characterised by goals, preferences and potentially relevant relationships with others, and the environment in which they are situated. The cases in which these activities have been systematically integrated for a mutual benefit are still very few compared to the corpus of crowd related literature. This paper presents a case study of an integrated approach to the definition of an innovative model for pedestrian and crowd simulation (on the side of synthesis) that was actually motivated and supported by the analyses of empirical data acquired from both experimental settings and observations in real world scenarios. In particular, we will introduce a model for the adaptive behaviour of pedestrians that are also members of groups, that strive to maintain their cohesion even in difficult (e.g., high density) situations. The paper will show how the synthesis phase also provided inputs to the analysis of empirical data, in a virtuous circle.

Connected through hyperedge HE-1 (induced by source paper "gd gan generative adversarial networks for trajectory prediction and group detection in crowds"):
  with members:
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "tracking by prediction a deep generative model for mutli person localisation and tracking"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "task specific visual saliency prediction with memory augmented conditional generative adversarial networks"
    - "learning temporal strategic relationships using generative adversarial imitation learning"
    - "tree memory networks for modelling long term temporal dependencies"
    - "soft hardwired attention an lstm framework for human trajectory prediction and abnormal event detection"
    - "infogail interpretable imitation learning from visual demonstrations"

Connected through hyperedge HE-2 (induced by source paper "a survey of recent advances in cnn based single image crowd counting and density estimation"):
  with members:
    - "switching convolutional neural network for crowd counting"
    - "cnn based cascaded multi task learning of high level prior and density estimation for crowd counting"
    - "resnetcrowd a residual deep learning architecture for crowd counting violent behaviour detection and crowd density level classification"
    - "beyond counting comparisons of density maps for crowd analysis tasks counting detection and tracking"
    - "mixture of counting cnns adaptive integration of cnns specialized to specific appearance for crowd counting"
    - "image de raining using a conditional generative adversarial network"
    - "a large contextual dataset for classification detection and counting of cars with deep learning"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-3 (induced by source paper "micro and macro pedestrian dynamics in counterflow the impact of social groups"):
  with members:
    - "investigating the effect of social groups in uni directional pedestrian flow"
    - "social groups and pedestrian crowds experiment on dyads in a counter flow scenario"

Connected through hyperedge HE-4 (induced by source paper "essentials of an integrated crowd management support system based on collective artificial intelligence"):
  with members:
    - "socially constrained structural learning for groups detection in crowd"

Connected through hyperedge HE-5 (induced by source paper "socially constrained structural learning for groups detection in crowd"):
  with members:
    - "a heat map based algorithm for recognizing group activities in videos"
    - "block coordinate frank wolfe optimization for structural svms"
    - "objective criteria for the evaluation of clustering methods"

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.MA(Multiagent Systems)