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: "asap accelerated short read alignment on programmable hardware"
  Abstract: The proliferation of high-throughput sequencing machines ensures rapid generation of up to billions of short nucleotide fragments in a short period of time. This massive amount of sequence data can quickly overwhelm today's storage and compute infrastructure. This paper explores the use of hardware acceleration to significantly improve the runtime of short-read alignment, a crucial step in preprocessing sequenced genomes. We focus on the Levenshtein distance (edit-distance) computation kernel and propose the ASAP accelerator, which utilizes the intrinsic delay of circuits for edit-distance computation elements as a proxy for computation. Our design is implemented on an Xilinx Virtex 7 FPGA in an IBM POWER8 system that uses the CAPI interface for cache coherence across the CPU and FPGA. Our design is $200\times$ faster than the equivalent C implementation of the kernel running on the host processor and $2.2\times$ faster for an end-to-end alignment tool for 120-150 base-pair short-read sequences. Further the design represents a $3760\times$ improvement over the CPU in performance/Watt terms.

Connected through hyperedge HE-1 (induced by source paper "inductive bias driven reinforcement learning for efficient schedules in heterogeneous clusters"):
  with members:
    - "learning scheduling algorithms for data processing clusters"
    - "relational inductive biases deep learning and graph networks"
    - "deep variational reinforcement learning for pomdps"
    - "qmdp net deep learning for planning under partial observability"
    - "on improving deep reinforcement learning for pomdps"
    - "the predictron end to end learning and planning"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "asynchronous methods for deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "bioseal in memory biological sequence alignment accelerator for large scale genomic data"):
  with members:
    - "a non volatile near memory read mapping accelerator"
    - "a resistive cam processing in storage architecture for dna sequence alignment"
    - "edit distance cannot be computed in strongly subquadratic time unless seth is false"

Connected through hyperedge HE-3 (induced by source paper "shouji a fast and efficient pre alignment filter for sequence alignment"):
  with members:
    - "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)