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: "vta an open hardware software stack for deep learning"
  Abstract: Specialized Deep Learning (DL) acceleration stacks, designed for a specific set of frameworks, model architectures, operators, and data types, offer the allure of high performance while sacrificing flexibility. Changes in algorithms, models, operators, or numerical systems threaten the viability of specialized hardware accelerators.  We propose VTA, a programmable deep learning architecture template designed to be extensible in the face of evolving workloads. VTA achieves this flexibility via a parametrizable architecture, two-level ISA, and a JIT compiler. The two-level ISA is based on (1) a task-ISA that explicitly orchestrates concurrent compute and memory tasks and (2) a microcode-ISA which implements a wide variety of operators with single-cycle tensor-tensor operations. Next, we propose a runtime system equipped with a JIT compiler for flexible code-generation and heterogeneous execution that enables effective use of the VTA architecture.  VTA is integrated and open-sourced into Apache TVM, a state-of-the-art deep learning compilation stack that provides flexibility for diverse models and divergent hardware backends. We propose a flow that performs design space exploration to generate a customized hardware architecture and software operator library that can be leveraged by mainstream learning frameworks. We demonstrate our approach by deploying optimized deep learning models used for object classification and style transfer on edge-class FPGAs.

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 "a data center fpga acceleration platform for convolutional neural networks"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "towards efficient convolutional neural network for domain specific applications on fpga"
    - "dissecting the nvidia volta gpu architecture via microbenchmarking"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "efficient processing of deep neural networks a tutorial and survey"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "ese efficient speech recognition engine with sparse lstm on fpga"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-3 (induced by source paper "surreal system fully integrated stack for distributed deep reinforcement learning"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "tvm end to end optimization stack for deep learning"
    - "distributed prioritized experience replay"
    - "deep reinforcement learning that matters"
    - "ray a distributed framework for emerging ai applications"
    - "rllib abstractions for distributed reinforcement learning"
    - "es is more than just a traditional finite difference approximator"
    - "deep neuroevolution genetic algorithms are a competitive alternative for training deep neural networks for reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "dnnvm end to end compiler leveraging heterogeneous optimizations on fpga based cnn accelerators"):
  with members:
    - "dla compiler and fpga overlay for neural network inference acceleration"
    - "tvm end to end optimization stack for deep learning"
    - "yolov3 an incremental improvement"
    - "toolflows for mapping convolutional neural networks on fpgas a survey and future directions"
    - "intel ngraph an intermediate representation compiler and executor for deep learning"
    - "towards efficient convolutional neural network for domain specific applications on fpga"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "optimizing cnn model inference on cpus"

Connected through hyperedge HE-5 (induced by source paper "gemmini an agile systolic array generator enabling systematic evaluations of deep learning architectures"):
  with members:
    - "integrating nvidia deep learning accelerator nvdla with risc v soc on firesim"
    - "fpsa a full system stack solution for reconfigurable reram based nn accelerator architecture"
    - "co design of deep neural nets and neural net accelerators for embedded vision applications"
    - "dnn dataflow choice is overrated"
    - "eyeriss v2 a flexible accelerator for emerging deep neural networks on mobile devices"
    - "deep learning inference in facebook data centers characterization performance optimizations and hardware implications"
    - "in datacenter performance analysis of a tensor processing unit"
    - "deep gradient compression reducing the communication bandwidth for distributed training"

Connected through hyperedge HE-6 (induced by source paper "swtvm exploring the automated compilation for deep learning on sunway architecture"):
  with members:
    - "glow graph lowering compiler techniques for neural networks"
    - "learning to optimize tensor programs"
    - "intel ngraph an intermediate representation compiler and executor for deep learning"
    - "tensor comprehensions framework agnostic high performance machine learning abstractions"
    - "dlvm a modern compiler infrastructure for deep learning systems"
    - "end to end learning for self driving cars"
    - "joint face detection and alignment using multitask cascaded convolutional networks"
    - "tensorflow a system for large scale machine learning"

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)