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: "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.

Connected through hyperedge HE-1 (induced by source paper "spatiotemporal co attention recurrent neural networks for human skeleton motion prediction"):
  with members:
    - "long term human motion prediction by modeling motion context and enhancing motion dynamic"
    - "learning and refining of privileged information based rnns for action recognition from depth sequences"
    - "modeling temporal dynamics and spatial configurations of actions using two stream recurrent neural networks"
    - "deep representation learning for human motion prediction and classification"
    - "skeleton based action recognition using spatio temporal lstm network with trust gates"
    - "on human motion prediction using recurrent neural networks"
    - "view adaptive recurrent neural networks for high performance human action recognition from skeleton data"
    - "spatio temporal lstm with trust gates for 3d human action recognition"

Connected through hyperedge HE-2 (induced by source paper "interaction relational network for mutual action recognition"):
  with members:
    - "action anticipation with rbf kernelized feature mapping rnn"
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "3d human pose estimation with relational networks"
    - "leveraging structural context models and ranking score fusion for human interaction prediction"
    - "skeleton based human action recognition with global context aware attention lstm networks"
    - "a simple neural network module for relational reasoning"
    - "sequential deep trajectory descriptor for action recognition with three stream cnn"

Connected through hyperedge HE-3 (induced by source paper "dynamically visual disambiguation of keyword based image search"):
  with members:
    - "deep representation learning for road detection through siamese network"
    - "towards automatic construction of diverse high quality image dataset"
    - "exploiting web images for dataset construction a domain robust approach"
    - "unsupervised visual sense disambiguation for verbs using multimodal embeddings"
    - "frankenstein learning deep face representations using small data"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "learning deep features for discriminative localization"
    - "very deep convolutional networks for large scale image recognition"

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)