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 "deep crowd flow prediction in built environments"):
  Abstract: Predicting the behavior of crowds in complex environments is a key requirement in a multitude of application areas, including crowd and disaster management, architectural design, and urban planning. Given a crowd's immediate state, current approaches simulate crowd movement to arrive at a future state. However, most applications require the ability to predict hundreds of possible simulation outcomes (e.g., under different environment and crowd situations) at real-time rates, for which these approaches are prohibitively expensive. #R##N#In this paper, we propose an approach to instantly predict the long-term flow of crowds in arbitrarily large, realistic environments. Central to our approach is a novel CAGE representation consisting of Capacity, Agent, Goal, and Environment-oriented information, which efficiently encodes and decodes crowd scenarios into compact, fixed-size representations that are environmentally lossless. We present a framework to facilitate the accurate and efficient prediction of crowd flow in never-before-seen crowd scenarios. We conduct a series of experiments to evaluate the efficacy of our approach and showcase positive results.

Includes members:
  Member "social ways learning multi modal distributions of pedestrian trajectories with gans":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "social wagdat interaction aware trajectory prediction via wasserstein graph double attention network")
  Member "human motion trajectory prediction a survey":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sequential forecasting of 100 000 points")
  Member "graphical contrastive losses for scene graph parsing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "unbiased scene graph generation from biased training")
  Member "decidenet counting varying density crowds through attention guided detection and density estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "a survey of recent advances in cnn based single image crowd counting and density estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "densely connected convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lossless compression of deep neural networks")
  Member "deep spatio temporal residual networks for citywide crowd flows prediction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "disentangling physical dynamics from unknown factors for unsupervised video prediction")
  Member "crowdnet a deep convolutional network for dense crowd counting":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ha ccn hierarchical attention based crowd counting network")

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.AI(Artificial Intelligence)