human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "near memory similarity search on automata processors"):
  Abstract: Embedded devices and multimedia applications today generate unprecedented volumes of data which must be indexed and made searchable. As a result, similarity search has become a critical idiom for many modern data intensive applications in natural language processing (NLP), vision, and robotics. At its core, similarity search is implemented using k-nearest neighbors (kNN) where computation consists of highly parallel distance calculations and a global top-k sort. In contemporary von-Neumann architectures, kNN is bottlenecked by data movement limiting throughput and latency. #R##N#Near-data processing has been proposed as a solution to reduce data movement for these memory bound applications improving run time and energy efficiency. In this paper, we codesign and evaluate kNN on the Micron Automata Processor (AP), which is a non-von Neumann architecture for near-data processing designed for automata evaluation. We present a novel nondeterministic finite automaton design and show how using temporal encodings it can be used to evaluate kNN. We evaluate our design's performance on the AP compared to state-of-the-art CPU, GPU, and FPGA implementations and show that the current generation AP hardware can achieve 52.6x speedup over multicore processors while maintaining competitive energy efficiency gains as heterogeneous computing substrates. Finally, we propose several automata optimizations techniques and simple architectural extensions, evaluate their potential impact, and show how they can achieve an additional 73.6x performance improvement on next generation AP hardware.

Includes members:
  Member "fast k nearest neighbor search using gpu":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep geometric 6 dof localization from a single image in topo metric maps")
  Member "hitting the memory wall implications of the obvious":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "flexible communication avoiding matrix multiplication on fpga with high level synthesis")

Question: Please tell me which class the source paper 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.DC(Distributed, Parallel, and Cluster Computing)