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: "approximate similarity search for online multimedia services on distributed cpu gpu platforms"
  Abstract: Similarity search in high-dimentional spaces is a pivotal operation found a variety of database applications. Recently, there has been an increase interest in similarity search for online content-based multimedia services. Those services, however, introduce new challenges with respect to the very large volumes of data that have to be indexed/searched, and the need to minimize response times observed by the end-users. Additionally, those users dynamically interact with the systems creating fluctuating query request rates, requiring the search algorithm to adapt in order to better utilize the underline hardware to reduce response times. In order to address these challenges, we introduce hypercurves, a flexible framework for answering approximate k-nearest neighbor (kNN) queries for very large multimedia databases, aiming at online content-based multimedia services. Hypercurves executes on hybrid CPU--GPU environments, and is able to employ those devices cooperatively to support massive query request rates. In order to keep the response times optimal as the request rates vary, it employs a novel dynamic scheduler to partition the work between CPU and GPU. Hypercurves was throughly evaluated using a large database of multimedia descriptors. Its cooperative CPU--GPU execution achieved performance improvements of up to 30x when compared to the single CPU-core version. The dynamic work partition mechanism reduces the observed query response times in about 50% when compared to the best static CPU--GPU task partition configuration. In addition, Hypercurves achieves superlinear scalability in distributed (multi-node) executions, while keeping a high guarantee of equivalence with its sequential version --- thanks to the proof of probabilistic equivalence, which supported its aggressive parallelization design.

Connected through hyperedge HE-1 (induced by source paper "fast approximate nearest neighbor search with navigating spreading out graphs"):
  with members:
    - "billion scale similarity search with gpus"
    - "efanna an extremely fast approximate nearest neighbor search algorithm based on knn graph"
    - "a revisit of hashing algorithms for approximate nearest neighbor search"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "approximate nearest neighbor search on high dimensional data experiments analyses and improvement v1 0"

Connected through hyperedge HE-2 (induced by source paper "pfo a parallelfriendly high performance system for online query and update of nearest neighbors"):
  with members:
    - "ringo interactive graph analytics on big memory machines"

Connected through hyperedge HE-3 (induced by source paper "performance analysis and efficient execution on systems with multi core cpus gpus and mics"):
  with members:
    - "performance evaluation of sparse matrix multiplication kernels on intel xeon phi"
    - "efficient irregular wavefront propagation algorithms on hybrid cpu gpu machines"

Connected through hyperedge HE-4 (induced by source paper "region templates data representation and management for large scale image analysis"):
  with members:
    - "comparative performance analysis of intel xeon phi gpu and cpu"
    - "efficient irregular wavefront propagation algorithms on hybrid cpu gpu machines"

Connected through hyperedge HE-5 (induced by source paper "scalable locality sensitive hashing for similarity search in high dimensional large scale multimedia datasets"):
  with members:
    - "accelerating nearest neighbor search on manycore systems"
    - "efficient irregular wavefront propagation algorithms on hybrid cpu gpu machines"
    - "searching in one billion vectors re rank with source coding"
    - "entropy based nearest neighbor search in high dimensions"

Connected through hyperedge HE-6 (induced by source paper "comparative performance analysis of intel xeon phi gpu and cpu"):
  with members:
    - "performance evaluation of sparse matrix multiplication kernels on intel xeon phi"
    - "efficient irregular wavefront propagation algorithms on hybrid cpu gpu machines"

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.MM(Multimedia)