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: "structural recurrent neural network srnn for group activity analysis"
  Abstract: A group of persons can be analyzed at various semantic levels such as individual actions, their interactions, and the activity of the entire group. In this paper, we propose a structural recurrent neural network (SRNN) that uses a series of interconnected RNNs to jointly capture the actions of individuals, their interactions, as well as the group activity. While previous structural recurrent neural networks assumed that the number of nodes and edges is constant, we use a grid pooling layer to address the fact that the number of individuals in a group can vary. We evaluate two variants of the structural recurrent neural network on the Volleyball Dataset.

Connected through hyperedge HE-1 (induced by source paper "progressive relation learning for group activity recognition"):
  with members:
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "learning actor relation graphs for group activity recognition"
    - "convolutional relational machine for group activity recognition"
    - "a comprehensive survey on graph neural networks"
    - "graph networks as learnable physics engines for inference and control"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "skeleton based action recognition with synchronous local and non local spatio temporal learning and frequency attention"
    - "visual semantic navigation using scene priors"

Connected through hyperedge HE-2 (induced by source paper "recurrent attentive neural process for sequential data"):
  with members:
    - "sequential neural processes"
    - "attentive neural processes"
    - "the functional neural process"
    - "neural processes"
    - "consistent generative query networks"
    - "conditional neural processes"
    - "attention is all you need"
    - "the statistical recurrent unit"

Connected through hyperedge HE-3 (induced by source paper "being the center of attention a person context cnn framework for personality recognition"):
  with members:
    - "persemon a deep network for joint analysis of apparent personality emotion and their relationship"
    - "rgb d based human motion recognition with deep learning a survey"
    - "a new representation of skeleton sequences for 3d action recognition"
    - "skepxels spatio temporal image representation of human skeleton joints for action recognition"
    - "social scene understanding end to end multi person action localization and collective activity recognition"
    - "realtime multi person 2d pose estimation using part affinity fields"
    - "salsa a novel dataset for multimodal group behavior analysis"
    - "learning deep features for discriminative localization"

Connected through hyperedge HE-4 (induced by source paper "multi level sequence gan for group activity recognition"):
  with members:
    - "tracking by prediction a deep generative model for mutli person localisation and tracking"
    - "discrimnet semi supervised action recognition from videos using generative adversarial networks"
    - "task specific visual saliency prediction with memory augmented conditional generative adversarial networks"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "semi supervised learning with context conditional generative adversarial networks"
    - "two stream lstm a deep fusion framework for human action recognition"
    - "cern confidence energy recurrent network for group activity recognition"
    - "pixel level domain transfer"

Connected through hyperedge HE-5 (induced by source paper "tri axial self attention for concurrent activity recognition"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"
    - "multi label class imbalanced action recognition in hockey videos via 3d convolutional neural networks"
    - "concurrent activity recognition with multimodal cnn lstm structure"
    - "disan directional self attention network for rnn cnn free language understanding"
    - "hollywood in homes crowdsourcing data collection for activity understanding"
    - "fully adaptive feature sharing in multi task networks with applications in person attribute classification"
    - "speed accuracy trade offs for modern convolutional object detectors"

Connected through hyperedge HE-6 (induced by source paper "hierarchical long short term concurrent memory for human interaction recognition"):
  with members:
    - "tracking the untrackable learning to track multiple cues with long term dependencies"
    - "spatial structural and temporal feature learning for human interaction prediction"
    - "deep structured models for group activity recognition"
    - "visual recognition by counting instances a multi instance cardinality potential kernel"
    - "structure inference machines recurrent neural networks for analyzing relations in group activity recognition"
    - "a hierarchical deep temporal model for group activity recognition"
    - "fast r cnn"
    - "differential recurrent neural networks for action 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)