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: "convolutional neural networks at constrained time cost"
  Abstract: Though recent advanced convolutional neural networks (CNNs) have been improving the image recognition accuracy, the models are getting more complex and time-consuming. For real-world applications in industrial and commercial scenarios, engineers and developers are often faced with the requirement of constrained time budget. In this paper, we investigate the accuracy of CNNs under constrained time cost. Under this constraint, the designs of the network architectures should exhibit as trade-offs among the factors like depth, numbers of filters, filter sizes, etc. With a series of controlled comparisons, we progressively modify a baseline model while preserving its time complexity. This is also helpful for understanding the importance of the factors in network designs. We present an architecture that achieves very competitive accuracy in the ImageNet dataset (11.8% top-5 error, 10-view test), yet is 20% faster than "AlexNet" (16.0% top-5 error, 10-view test).

Connected through hyperedge HE-1 (induced by source paper "semantic image networks for human action recognition"):
  with members:
    - "spatiotemporal pyramid network for video action recognition"
    - "multi fiber networks for video recognition"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "spatio temporal channel correlation networks for action classification"
    - "a 2 nets double attention networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "appearance and relation networks for video classification"

Connected through hyperedge HE-2 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming"):
  with members:
    - "learning to detect"
    - "learning optimal resource allocations in wireless systems"
    - "power control in cellular massive mimo with varying user activity a deep learning solution"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "cnn based signal detection for banded linear systems"
    - "deep learning coordinated beamforming for highly mobile millimeter wave systems"
    - "deep learning for radio resource allocation in multi cell networks"

Connected through hyperedge HE-3 (induced by source paper "learning the mapping mathbf x mapsto sum_ i 1 d x_i 2 the cost of finding the needle in a haystack"):
  with members:
    - "explaining landscape connectivity of low cost solutions for multilayer nets"
    - "uniform convergence may be unable to explain generalization in deep learning"
    - "barron spaces and the compositional function spaces for neural network models"
    - "a priori estimates of the population risk for residual networks"
    - "deep sets"
    - "rethinking the value of network pruning"
    - "expressive power of recurrent neural networks"
    - "error bounds for approximations with deep relu networks"

Connected through hyperedge HE-4 (induced by source paper "hyperparameters optimization for deep learning based emotion prediction for human robot interaction"):
  with members:
    - "affectnet a database for facial expression valence and arousal computing in the wild"
    - "social robots for people with developmental disabilities a user study on design features of a graphical user interface"
    - "i know how you feel emotion recognition with facial landmarks"
    - "real time convolutional neural networks for emotion and gender classification"
    - "convolutional neural networks for facial expression recognition"
    - "facial emotion detection using convolutional neural networks and representational autoencoder units"
    - "going deeper in facial expression recognition using deep neural networks"
    - "modeling spatial and temporal cues for multi label facial action unit detection"

Connected through hyperedge HE-5 (induced by source paper "pelican a deep residual network for network intrusion detection"):
  with members:
    - "a transfer learning approach for network intrusion detection"
    - "deep residual learning for image recognition"
    - "gated feedback recurrent neural networks"
    - "going deeper with convolutions"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"

Connected through hyperedge HE-6 (induced by source paper "convolution with even sized kernels and symmetric padding"):
  with members:
    - "the gan landscape losses architectures regularization and normalization"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "relational deep reinforcement learning"
    - "training and inference with integers in deep neural networks"
    - "regularized evolution for image classifier architecture search"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "hetconv heterogeneous kernel based convolutions for deep cnns"):
  with members:
    - "stability based filter pruning for accelerating deep cnns"
    - "multi layer pruning framework for compressing single shot multibox detector"
    - "differential attention for visual question answering"
    - "leveraging filter correlations for deep model compression"
    - "soft filter pruning for accelerating deep convolutional neural networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "nisp pruning networks using neuron importance score propagation"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-8 (induced by source paper "a spectral nonlocal block for neural networks"):
  with members:
    - "compact global descriptor for neural networks"
    - "lightweight network architecture for real time action recognition"
    - "neural dynamics discovery via gaussian process recurrent neural networks"
    - "scan self and collaborative attention network for video person re identification"
    - "a comprehensive survey on graph neural networks"
    - "stochastic trajectory prediction with social graph network"
    - "ccnet criss cross attention for semantic segmentation"
    - "compact generalized non local network"

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)