human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "automated lane detection in crowds using proximity graphs"):
  Abstract: Studying the behavior of crowds is vital for understanding and predicting human interactions in public areas. Research has shown that, under certain conditions, large groups of people can form collective behavior patterns: local interactions between individuals results in global movements patterns. To detect these patterns in a crowd, we assume each person is carrying an on-body device that acts a local proximity sensor, e.g., smartphone or bluetooth badge, and represent the texture of the crowd as a proximity graph. Our goal is extract information about crowds from these proximity graphs. In this work, we focus on one particular type of pattern: lane formation. We present a formal definition of a lane, proposed a simple probabilistic model that simulates lanes moving through a stationary crowd, and present an automated lane-detection method. Our preliminary results show that our method is able to detect lanes of different shapes and sizes. We see our work as an initial step towards rich pattern recognition using proximity graphs.

Includes members:
  Member "crowded scene analysis a survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "fully convolutional neural networks for crowd segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pdanet pyramid density aware attention net for accurate crowd counting")
  Member "spring embedders and force directed graph drawing algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to segment 3d point clouds in 2d image space")

Question: Please tell me which class the source paper 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)