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: "socially constrained structural learning for groups detection in crowd"
  Abstract: Modern crowd theories agree that collective behavior is the result of the underlying interactions among small groups of individuals. In this work, we propose a novel algorithm for detecting social groups in crowds by means of a Correlation Clustering procedure on people trajectories. The affinity between crowd members is learned through an online formulation of the Structural SVM framework and a set of specifically designed features characterizing both their physical and social identity, inspired by Proxemic theory, Granger causality, DTW and Heat-maps. To adhere to sociological observations, we introduce a loss function (G-MITRE) able to deal with the complexity of evaluating group detection performances. We show our algorithm achieves state-of-the-art results when relying on both ground truth trajectories and tracklets previously extracted by available detector/tracker systems.

Connected through hyperedge HE-1 (induced by source paper "group reidentification with multigrained matching and integration"):
  with members:
    - "triplet based deep similarity learning for person re identification"
    - "unsupervised deep domain adaptation for pedestrian detection"
    - "in defense of the triplet loss for person re identification"
    - "learning deep neural networks for vehicle re id with visual spatio temporal path proposals"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "person re identification by deep joint learning of multi loss classification"
    - "alignedreid surpassing human level performance in person re identification"
    - "learning correspondence structures for person re identification"

Connected through hyperedge HE-2 (induced by source paper "deep ranking model by large adaptive margin learning for person re identification"):
  with members:
    - "a discriminatively learned cnn embedding for person reidentification"
    - "re ranking person re identification with k reciprocal encoding"
    - "gated siamese convolutional neural network architecture for human re identification"
    - "learning a discriminative null space for person re identification"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "a siamese long short term memory architecture for human re identification"
    - "deep recurrent convolutional networks for video based person re identification an end to end approach"
    - "top push video based person re identification"

Connected through hyperedge HE-3 (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-4 (induced by source paper "linear maximum margin classifier for learning from uncertain data"):
  with members:
    - "learning to detect video events from zero or very few video examples"
    - "going deeper with convolutions"
    - "imagenet large scale visual recognition challenge"
    - "representation learning a review and new perspectives"
    - "robustness and generalization"
    - "robustness and regularization of support vector machines"

Connected through hyperedge HE-5 (induced by source paper "binary quadratic programing for online tracking of hundreds of people in extremely crowded scenes"):
  with members:
    - "high speed tracking with kernelized correlation filters"
    - "on pairwise costs for network flow multi object tracking"

Connected through hyperedge HE-6 (induced by source paper "essentials of an integrated crowd management support system based on collective artificial intelligence"):
  with members:
    - "towards an integrated approach to crowd analysis and crowd synthesis"

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)