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 "hierarchical long short term concurrent memory for human interaction recognition"):
  Abstract: In this paper, we aim to address the problem of human interaction recognition in videos by exploring the long-term inter-related dynamics among multiple persons. Recently, Long Short-Term Memory (LSTM) has become a popular choice to model individual dynamic for single-person action recognition due to its ability of capturing the temporal motion information in a range. However, existing RNN models focus only on capturing the dynamics of human interaction by simply combining all dynamics of individuals or modeling them as a whole. Such models neglect the inter-related dynamics of how human interactions change over time. To this end, we propose a novel Hierarchical Long Short-Term Concurrent Memory (H-LSTCM) to model the long-term inter-related dynamics among a group of persons for recognizing the human interactions. Specifically, we first feed each person's static features into a Single-Person LSTM to learn the single-person dynamic. Subsequently, the outputs of all Single-Person LSTM units are fed into a novel Concurrent LSTM (Co-LSTM) unit, which mainly consists of multiple sub-memory units, a new cell gate and a new co-memory cell. In a Co-LSTM unit, each sub-memory unit stores individual motion information, while this Co-LSTM unit selectively integrates and stores inter-related motion information between multiple interacting persons from multiple sub-memory units via the cell gate and co-memory cell, respectively. Extensive experiments on four public datasets validate the effectiveness of the proposed H-LSTCM by comparing against baseline and state-of-the-art methods.

Includes members:
  Member "structural recurrent neural network srnn for group activity analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "progressive relation learning for group activity recognition")
  Member "tracking the untrackable learning to track multiple cues with long term dependencies":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multiple object tracking by flowing and fusing")
  Member "spatial structural and temporal feature learning for human interaction prediction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "concurrence aware long short term sub memories for person person action recognition")
  Member "deep structured models for group activity recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "captioning near future activity sequences")
  Member "visual recognition by counting instances a multi instance cardinality potential kernel":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning actor relation graphs for group activity recognition")
  Member "structure inference machines recurrent neural networks for analyzing relations in group activity recognition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning actor relation graphs for group activity recognition")
  Member "a hierarchical deep temporal model for group activity recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction")
  Member "fast r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")

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)