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: "kickstarting high performance energy efficient manycore architectures with epiphany"
  Abstract: In this paper we introduce Epiphany as a high-performance energy-efficient manycore architecture suitable for real-time embedded systems. This scalable architecture supports floating point operations in hardware and achieves 50 GFLOPS/W in 28 nm technology, making it suitable for high performance streaming applications like radio base stations and radar signal processing. Through an efficient 2D mesh Network-on-Chip and a distributed shared memory model, the architecture is scalable to thousands of cores on a single chip. An Epiphany-based open source computer named Parallella was launched in 2012 through Kickstarter crowd funding and has now shipped to thousands of customers around the world.

Connected through hyperedge HE-1 (induced by source paper "a survey on distributed machine learning"):
  with members:
    - "mxnet mpi embedding mpi parallelism in parameter server task model for scaling deep learning"
    - "how to backdoor federated learning"
    - "no peek a survey of private distributed deep learning"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "poseidon an efficient communication architecture for distributed deep learning on gpu clusters"
    - "scaling deep learning on gpu and knights landing clusters"
    - "revisiting distributed synchronous sgd"
    - "accurate large minibatch sgd training imagenet in 1 hour"

Connected through hyperedge HE-2 (induced by source paper "adaptive motor control and learning in a spiking neural network realised on a mixed signal neuromorphic processor"):
  with members:
    - "the event camera dataset and simulator event based data for pose estimation visual odometry and slam"
    - "a scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors dynaps"
    - "event driven random back propagation enabling neuromorphic deep learning machines"
    - "aggressive quadrotor flight through narrow gaps with onboard sensing and computing using active vision"
    - "learning and inferring relations in cortical networks"
    - "precise deep neural network computation on imprecise low power analog hardware"

Connected through hyperedge HE-3 (induced by source paper "a distributed shared memory model and c templated meta programming interface for the epiphany risc array processor"):
  with members:
    - "opencl openshmem hybrid programming model for the adapteva epiphany architecture"
    - "implementing openshmem for the adapteva epiphany risc array processor"
    - "an openshmem implementation for the adapteva epiphany coprocessor"
    - "advances in run time performance and interoperability for the adapteva epiphany coprocessor"
    - "programming the adapteva epiphany 64 core network on chip coprocessor"

Connected through hyperedge HE-4 (induced by source paper "symmetry in software synthesis"):
  with members:
    - "graph isomorphism in quasipolynomial time"
    - "programming the adapteva epiphany 64 core network on chip coprocessor"
    - "practical graph isomorphism ii"
    - "conauto 2 0 fast isomorphism testing and automorphism group computation"

Connected through hyperedge HE-5 (induced by source paper "i can has supercomputer a novel approach to teaching parallel and distributed computing concepts using a meme based programming language"):
  with members:
    - "an openshmem implementation for the adapteva epiphany coprocessor"
    - "advances in run time performance and interoperability for the adapteva epiphany coprocessor"

Connected through hyperedge HE-6 (induced by source paper "opencl openshmem hybrid programming model for the adapteva epiphany architecture"):
  with members:
    - "an openshmem implementation for the adapteva epiphany coprocessor"
    - "programming the adapteva epiphany 64 core network on chip coprocessor"

Connected through hyperedge HE-7 (induced by source paper "bulk synchronous pseudo streaming algorithms for many core accelerators"):
  with members:
    - "parallel programming model for the epiphany many core coprocessor using threaded mpi"
    - "sampling in space restricted settings"

Connected through hyperedge HE-8 (induced by source paper "generation of the single precision blas library for the parallella platform with epiphany co processor acceleration using the blis framework"):
  with members:
    - "parallel programming model for the epiphany many core coprocessor using threaded mpi"
    - "programming the adapteva epiphany 64 core network on chip coprocessor"

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.AR(Hardware Architecture)