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: "programming the adapteva epiphany 64 core network on chip coprocessor"
  Abstract: In the construction of exascale computing systems energy efficiency and power consumption are two of the major challenges. Low-power high performance embedded systems are of increasing interest as building blocks for large scale high- performance systems. However, extracting maximum performance out of such systems presents many challenges. Various aspects from the hardware architecture to the programming models used need to be explored. The Epiphany architecture integrates low-power RISC cores on a 2D mesh network and promises up to 70 GFLOPS/Watt of processing efficiency. However, with just 32 KB of memory per eCore for storing both data and code, and only low level inter-core communication support, programming the Epiphany system presents several challenges. In this paper we evaluate the performance of the Epiphany system for a variety of basic compute and communication operations. Guided by this data we explore strategies for implementing scientific applications on memory constrained low-powered devices such as the Epiphany. With future systems expected to house thousands of cores in a single chip, the merits of such architectures as a path to exascale is compared to other competing systems.

Connected through hyperedge HE-1 (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"
    - "kickstarting high performance energy efficient manycore architectures with epiphany"

Connected through hyperedge HE-2 (induced by source paper "symmetry in software synthesis"):
  with members:
    - "graph isomorphism in quasipolynomial time"
    - "kickstarting high performance energy efficient manycore architectures with epiphany"
    - "practical graph isomorphism ii"
    - "conauto 2 0 fast isomorphism testing and automorphism group computation"

Connected through hyperedge HE-3 (induced by source paper "opencl openshmem hybrid programming model for the adapteva epiphany architecture"):
  with members:
    - "an openshmem implementation for the adapteva epiphany coprocessor"
    - "kickstarting high performance energy efficient manycore architectures with epiphany"

Connected through hyperedge HE-4 (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"
    - "kickstarting high performance energy efficient manycore architectures with epiphany"

Connected through hyperedge HE-5 (induced by source paper "parallel programming model for the epiphany many core coprocessor using threaded mpi"):
  with members:
    - "kickstarting high performance energy efficient manycore architectures with epiphany"

Connected through hyperedge HE-6 (induced by source paper "kickstarting high performance energy efficient manycore architectures with epiphany"):
  with members:
    - "picoarray technology the tool s story"

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)