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: "a non volatile near memory read mapping accelerator"
  Abstract: DNA sequencing is the physical or biochemical process of identifying the location of the four bases (Adenine, Guanine, Cytosine, Thymine) in a DNA strand. As semiconductor technology revolutionized computing, DNA sequencing technology (termed Next Generation Sequencing, NGS) revolutionized genomic research. Modern NGS platforms can sequence hundreds of millions of short DNA fragments in parallel. The output short DNA fragments from NGS platforms are termed reads. Mapping each output read to a reference genome of the same species (i.e., read mapping) is a common critical first step in a rich and diverse set of emerging bioinformatics applications. The importance of read mapping motivated various sequence alignment and mapping algorithms, which start to fall short of tackling the growing scale of the problem. Mapping represents a search-heavy memory-intensive operation and barely requires complex floating point arithmetic, therefore, can greatly benefit from in- or near-memory processing, where non-volatile memory can accommodate the large memory footprint in an area and energy efficient manner. This paper introduces a scalable, energy-efficient high-throughput near (non-volatile) memory read mapping accelerator: BioMAP. Instead of optimizing an algorithm developed for general-purpose computers or GPUs, BioMAP rethinks the algorithm and accelerator design together from the ground up. Thereby BioMAP can improve the throughput of read mapping by 4.0 times while reducing the energy consumption by 26.2 times when compared to a highly-optimized algorithm for modern GPUs.

Connected through hyperedge HE-1 (induced by source paper "bioseal in memory biological sequence alignment accelerator for large scale genomic data"):
  with members:
    - "asap accelerated short read alignment on programmable hardware"
    - "a resistive cam processing in storage architecture for dna sequence alignment"
    - "edit distance cannot be computed in strongly subquadratic time unless seth is false"

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.DC(Distributed, Parallel, and Cluster Computing)