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: "sgad soft guided adaptively dropped neural network"
  Abstract: Deep neural networks (DNNs) have been proven to have many redundancies. Hence, many efforts have been made to compress DNNs. However, the existing model compression methods treat all the input samples equally while ignoring the fact that the difficulties of various input samples being correctly classified are different. To address this problem, DNNs with adaptive dropping mechanism are well explored in this work. To inform the DNNs how difficult the input samples can be classified, a guideline that contains the information of input samples is introduced to improve the performance. Based on the developed guideline and adaptive dropping mechanism, an innovative soft-guided adaptively-dropped (SGAD) neural network is proposed in this paper. Compared with the 32 layers residual neural networks, the presented SGAD can reduce the FLOPs by 77% with less than 1% drop in accuracy on CIFAR-10.

Connected through hyperedge HE-1 (induced by source paper "batch shaping for learning conditional channel gated networks"):
  with members:
    - "sbnet sparse blocks network for fast inference"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "alleviating catastrophic forgetting using context dependent gating and synaptic stabilization"
    - "compressing neural networks using the variational information bottleneck"
    - "gaternet dynamic filter selection in convolutional neural network via a dedicated global gating network"
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "dynamic channel pruning feature boosting and suppression"
    - "skipnet learning dynamic routing in convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "design light weight 3d convolutional networks for video recognition temporal residual fully separable block and fast algorithm"):
  with members:
    - "slowfast networks for video recognition"
    - "rethinking spatiotemporal feature learning speed accuracy trade offs in video classification"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "convolutional two stream network fusion for video action recognition"
    - "spatiotemporal residual networks for video action recognition"
    - "long term temporal convolutions for action recognition"
    - "deep residual learning for image recognition"
    - "fast r cnn"

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.LG(Machine Learning)