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: "learning to detect violent videos using convolutional long short term memory"
  Abstract: Developing a technique for the automatic analysis of surveillance videos in order to identify the presence of violence is of broad interest. In this work, we propose a deep neural network for the purpose of recognizing violent videos. A convolutional neural network is used to extract frame level features from a video. The frame level features are then aggregated using a variant of the long short term memory that uses convolutional gates. The convolutional neural network along with the convolutional long short term memory is capable of capturing localized spatio-temporal features which enables the analysis of local motion taking place in the video. We also propose to use adjacent frame differences as the input to the model thereby forcing it to encode the changes occurring in the video. The performance of the proposed feature extraction pipeline is evaluated on three standard benchmark datasets in terms of recognition accuracy. Comparison of the results obtained with the state of the art techniques revealed the promising capability of the proposed method in recognizing violent videos.

Connected through hyperedge HE-1 (induced by source paper "vision based fight detection from surveillance cameras"):
  with members:
    - "real world anomaly detection in surveillance videos"
    - "attention is all you need"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "image captioning with semantic attention"
    - "an end to end spatio temporal attention model for human action recognition from skeleton data"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "xception deep learning with depthwise separable convolutions"
    - "show attend and tell neural image caption generation with visual attention"

Connected through hyperedge HE-2 (induced by source paper "long short term memory networks in memristor crossbar arrays"):
  with members:
    - "mixed precision in memory computing"
    - "approximate fpga based lstms under computation time constraints"
    - "chipmunk a systolically scalable 0 9 mm 2 3 08 gop s mw 1 2 mw accelerator for near sensor recurrent neural network inference"
    - "the microsoft 2017 conversational speech recognition system"
    - "data recombination for neural semantic parsing"
    - "acceleration of deep neural network training with resistive cross point devices design considerations"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "training and operation of an integrated neuromorphic network based on metal oxide memristors"

Connected through hyperedge HE-3 (induced by source paper "predicting tongue motion in unlabeled ultrasound videos using convolutional lstm neural network"):
  with members:
    - "decomposing motion and content for natural video sequence prediction"
    - "updating the silent speech challenge benchmark with deep learning"

Connected through hyperedge HE-4 (induced by source paper "top down attention recurrent vlad encoding for action recognition in videos"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "deep visual attention prediction"
    - "adding attentiveness to the neurons in recurrent neural networks"
    - "attention is all we need nailing down object centric attention for egocentric activity recognition"
    - "lsta long short term attention for egocentric action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "actionvlad learning spatio temporal aggregation for action classification"

Connected through hyperedge HE-5 (induced by source paper "attention is all we need nailing down object centric attention for egocentric activity recognition"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "anomaly detection in video using predictive convolutional long short term memory networks"
    - "going deeper into first person activity recognition"
    - "pooled motion features for first person videos"
    - "long term recurrent convolutional networks for visual recognition and description"
    - "two stream convolutional networks for action recognition in videos"

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)