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 deep temporal models for group activity recognition"
  Abstract: In this paper we present an approach for classifying the activity performed by a group of people in a video sequence. This problem of group activity recognition can be addressed by examining individual person actions and their relations. Temporal dynamics exist both at the level of individual person actions as well as at the level of group activity. Given a video sequence as input, methods can be developed to capture these dynamics at both person-level and group-level detail. We build a deep model to capture these dynamics based on LSTM (long short-term memory) models. In order to model both person-level and group-level dynamics, we present a 2-stage deep temporal model for the group activity recognition problem. In our approach, one LSTM model is designed to represent action dynamics of individual people in a video sequence and another LSTM model is designed to aggregate person-level information for group activity recognition. We collected a new dataset consisting of volleyball videos labeled with individual and group activities in order to evaluate our method. Experimental results on this new Volleyball Dataset and the standard benchmark Collective Activity Dataset demonstrate the efficacy of the proposed models.

Connected through hyperedge HE-1 (induced by source paper "structural recurrent neural network srnn for group activity analysis"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "cern confidence energy recurrent network for group activity recognition"
    - "social scene understanding end to end multi person action localization and collective activity recognition"
    - "deep structured models for group activity recognition"
    - "structure inference machines recurrent neural networks for analyzing relations in group activity recognition"
    - "a hierarchical deep temporal model for group activity recognition"
    - "structural rnn deep learning on spatio temporal graphs"
    - "detecting events and key actors in multi person videos"

Connected through hyperedge HE-2 (induced by source paper "cern confidence energy recurrent network for group activity recognition"):
  with members:
    - "semantic object parsing with graph lstm"
    - "energy based generative adversarial network"
    - "deep markov random field for image modeling"
    - "structured prediction energy networks"
    - "visual recognition by counting instances a multi instance cardinality potential kernel"
    - "conditional random fields as recurrent neural networks"
    - "structure inference machines recurrent neural networks for analyzing relations in group activity recognition"
    - "a hierarchical deep temporal model for group activity recognition"

Connected through hyperedge HE-3 (induced by source paper "active learning for structured prediction from partially labeled data"):
  with members:
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "discovering human interactions in videos with limited data labeling"
    - "deep structured models for group activity recognition"
    - "structure inference machines recurrent neural networks for analyzing relations in group activity recognition"
    - "detecting events and key actors in multi person videos"
    - "joint inference of groups events and human roles in aerial videos"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "caffe convolutional architecture for fast feature embedding"

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)