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 "pedestrian trajectory prediction with structured memory hierarchies"):
  Abstract: This paper presents a novel framework for human trajectory prediction based on multimodal data (video and radar). Motivated by recent neuroscience discoveries, we propose incorporating a structured memory component in the human trajectory prediction pipeline to capture historical information to improve performance. We introduce structured LSTM cells for modelling the memory content hierarchically, preserving the spatiotemporal structure of the information and enabling us to capture both short-term and long-term context. We demonstrate how this architecture can be extended to integrate salient information from multiple modalities to automatically store and retrieve important information for decision making without any supervision. We evaluate the effectiveness of the proposed models on a novel multimodal dataset that we introduce, consisting of 40,000 pedestrian trajectories, acquired jointly from a radar system and a CCTV camera system installed in a public place. The performance is also evaluated on the publicly available New York Grand Central pedestrian database. In both settings, the proposed models demonstrate their capability to better anticipate future pedestrian motion compared to existing state of the art.

Includes members:
  Member "tracking by prediction a deep generative model for mutli person localisation and tracking":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multiple object tracking by flowing and fusing")
  Member "understanding human behaviors in crowds by imitating the decision making process":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to simulate human movement")
  Member "task specific visual saliency prediction with memory augmented conditional generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "exploiting human social cognition for the detection of fake and fraudulent faces via memory networks")
  Member "learning temporal strategic relationships using generative adversarial imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "exploiting human social cognition for the detection of fake and fraudulent faces via memory networks")
  Member "efficient large scale multi modal classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "what makes training multi modal classification networks hard")
  Member "context aware trajectory prediction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mcenet multi context encoder network for homogeneous agent trajectory prediction in mixed traffic")
  Member "neural map structured memory for deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework")
  Member "tree memory networks for modelling long term temporal dependencies":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "exploiting human social cognition for the detection of fake and fraudulent faces via memory networks")

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)