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: "theano based large scale visual recognition with multiple gpus"
  Abstract: In this report, we describe a Theano-based AlexNet (Krizhevsky et al., 2012) implementation and its naive data parallelism on multiple GPUs. Our performance on 2 GPUs is comparable with the state-of-art Caffe library (Jia et al., 2014) run on 1 GPU. To the best of our knowledge, this is the first open-source Python-based AlexNet implementation to-date.

Connected through hyperedge HE-1 (induced by source paper "improving transparency of deep neural inference process"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "visual analytics in deep learning an interrogative survey for the next frontiers"
    - "methods for interpreting and understanding deep neural networks"
    - "open problems in engineering and quality assurance of safety critical machine learning systems"
    - "visual analytics for explainable deep learning"
    - "explainable software analytics"
    - "network dissection quantifying interpretability of deep visual representations"
    - "explaining nonlinear classification decisions with deep taylor decomposition"

Connected through hyperedge HE-2 (induced by source paper "a detailed comparative study of open source deep learning frameworks"):
  with members:
    - "comparative study of deep learning software frameworks"
    - "benchmarking state of the art deep learning software tools"
    - "a tour of tensorflow"
    - "theano a python framework for fast computation of mathematical expressions"
    - "tensorflow a system for large scale machine learning"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (induced by source paper "embedding visual hierarchy with deep networks for large scale visual recognition"):
  with members:
    - "robust and discriminative labeling for multi label active learning based on maximum correntropy criterion"
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "yolo9000 better faster stronger"
    - "hypernet towards accurate region proposal generation and joint object detection"
    - "deepsaliency multi task deep neural network model for salient object detection"
    - "deep residual learning for image recognition"
    - "how transferable are features in deep neural networks"
    - "going deeper with convolutions"

Connected through hyperedge HE-4 (induced by source paper "network analysis for explanation"):
  with members:
    - "network dissection quantifying interpretability of deep visual representations"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "not just a black box learning important features through propagating activation differences"
    - "imagenet large scale visual recognition challenge"
    - "show and tell a neural image caption generator"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "exploring the design space of deep convolutional neural networks at large scale"):
  with members:
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "origami a 803 gop s w convolutional network accelerator"
    - "systematic evaluation of convolution neural network advances on the imagenet"
    - "theano mpi a theano based distributed training framework"
    - "distributed deep learning using synchronous stochastic gradient descent"
    - "ristretto hardware oriented approximation of convolutional neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "how to scale distributed deep learning"

Connected through hyperedge HE-6 (induced by source paper "theano mpi a theano based distributed training framework"):
  with members:
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "firecaffe near linear acceleration of deep neural network training on compute clusters"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "one weird trick for parallelizing convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "multi way multilingual neural machine translation with a shared attention mechanism"):
  with members:
    - "improving neural machine translation models with monolingual data"
    - "blocks and fuel frameworks for deep learning"
    - "effective approaches to attention based neural machine translation"
    - "neural machine translation of rare words with subword units"
    - "on using monolingual corpora in neural machine translation"
    - "multi task sequence to sequence learning"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-8 (induced by source paper "cost sensitive deep learning with layer wise cost estimation"):
  with members:
    - "improving object detection with deep convolutional networks via bayesian optimization and structured prediction"
    - "deep residual learning for image recognition"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "cost aware pre training for multiclass cost sensitive deep learning"
    - "empirical evaluation of rectified activations in convolutional network"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "overfeat integrated recognition localization and detection using convolutional networks"

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)