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: "comparative study of caffe neon theano and torch for deep learning"
  Abstract: Deep learning methods have resulted in significant performance improvements in several application domains and as such several software frameworks have been developed to facilitate their implementation. This paper presents a comparative study of four deep learning frameworks, namely Caffe, Neon, Theano, and Torch, on three aspects: extensibility, hardware utilization, and speed. The study is performed on several types of deep learning architectures and we evaluate the performance of the above frameworks when employed on a single machine for both (multi-threaded) CPU and GPU (Nvidia Titan X) settings. The speed performance metrics used here include the gradient computation time, which is important during the training phase of deep networks, and the forward time, which is important from the deployment perspective of trained networks. For convolutional networks, we also report how each of these frameworks support various convolutional algorithms and their corresponding performance. From our experiments, we observe that Theano and Torch are the most easily extensible frameworks. We observe that Torch is best suited for any deep architecture on CPU, followed by Theano. It also achieves the best performance on the GPU for large convolutional and fully connected networks, followed closely by Neon. Theano achieves the best performance on GPU for training and deployment of LSTM networks. Finally Caffe is the easiest for evaluating the performance of standard deep architectures.

Connected through hyperedge HE-1 (induced by source paper "the deep learning compiler a comprehensive survey"):
  with members:
    - "shredder learning noise distributions to protect inference privacy"
    - "fusionstitching boosting execution efficiency of memory intensive computations for dl workloads"
    - "stripe tensor compilation via the nested polyhedral model"
    - "tensorflow eager a multi stage python embedded dsl for machine learning"
    - "privacy for rescue a new testimony why privacy is vulnerable in deep models"
    - "dissecting the graphcore ipu architecture via microbenchmarking"
    - "benchmarking tpu gpu and cpu platforms for deep learning"
    - "a comprehensive survey on graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "an empirical study towards characterizing deep learning development and deployment across different frameworks and platforms"):
  with members:
    - "moving deep learning into web browser how far can we go"
    - "deep learning towards mobile applications"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "deepmutation mutation testing of deep learning systems"
    - "a first look at deep learning apps on smartphones"
    - "lstm a search space odyssey"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "deepxplore automated whitebox testing of deep learning systems"

Connected through hyperedge HE-3 (induced by source paper "performance and power evaluation of ai accelerators for training deep learning models"):
  with members:
    - "automl a survey of the state of the art"
    - "the impact of gpu dvfs on the energy and performance of deep learning an empirical study"
    - "benchmarking tpu gpu and cpu platforms for deep learning"
    - "image classification at supercomputer scale"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "intel ngraph an intermediate representation compiler and executor for deep learning"
    - "in datacenter performance analysis of a tensor processing unit"
    - "gpgpu performance estimation with core and memory frequency scaling"

Connected through hyperedge HE-4 (induced by source paper "benchmarking tpu gpu and cpu platforms for deep learning"):
  with members:
    - "scalable methods for 8 bit training of neural networks"
    - "ai matrix synthetic benchmarks for dnn"
    - "beyond data and model parallelism for deep neural networks"
    - "tvm end to end optimization stack for deep learning"
    - "glow graph lowering compiler techniques for neural networks"
    - "measuring the effects of data parallelism on neural network training"
    - "in datacenter performance analysis of a tensor processing unit"
    - "deep gradient compression reducing the communication bandwidth for distributed training"

Connected through hyperedge HE-5 (induced by source paper "benchmarking the performance and power of ai accelerators for ai training"):
  with members:
    - "automl a survey of the state of the art"
    - "the impact of gpu dvfs on the energy and performance of deep learning an empirical study"
    - "benchmarking tpu gpu and cpu platforms for deep learning"
    - "image classification at supercomputer scale"
    - "intel ngraph an intermediate representation compiler and executor for deep learning"
    - "highly scalable deep learning training system with mixed precision training imagenet in four minutes"
    - "gpgpu performance estimation with core and memory frequency scaling"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "exploiting parallelism opportunities with deep learning frameworks"):
  with members:
    - "the architectural implications of facebook s dnn based personalized recommendation"
    - "deep learning recommendation model for personalization and recommendation systems"
    - "auto tuning tensorflow threading model for cpu backend"
    - "bandana using non volatile memory for storing deep learning models"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "attention is all you need"
    - "neural collaborative filtering"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-7 (induced by source paper "frustrated with replicating claims of a shared model a solution"):
  with members:
    - "analysis of dawnbench a time to accuracy machine learning performance benchmark"
    - "lstm benchmarks for deep learning frameworks"
    - "latency and throughput characterization of convolutional neural networks for mobile computer vision"
    - "characterizing co located datacenter workloads an alibaba case study"
    - "tbd benchmarking and analyzing deep neural network training"
    - "tensorflow serving flexible high performance ml serving"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "dlpaper2code auto generation of code from deep learning research papers"

Connected through hyperedge HE-8 (induced by source paper "a survey on deep learning toolkits and libraries for intelligent user interfaces"):
  with members:
    - "survey of the state of the art in natural language generation core tasks applications and evaluation"
    - "intelligent user interfaces a tutorial"
    - "machine teaching a new paradigm for building machine learning systems"
    - "rasa open source language understanding and dialogue management"
    - "end to end optimization of goal driven and visually grounded dialogue systems"
    - "first person action object detection with egonet"
    - "benchmarking state of the art deep learning software tools"
    - "guesswhat visual object discovery through multi modal dialogue"

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)