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: "faster and more accurate sequence alignment with snap"
  Abstract: We present the Scalable Nucleotide Alignment Program (SNAP), a new short and long read aligner that is both more accurate (i.e., aligns more reads with fewer errors) and 10-100x faster than state-of-the-art tools such as BWA. Unlike recent aligners based on the Burrows-Wheeler transform, SNAP uses a simple hash index of short seed sequences from the genome, similar to BLAST's. However, SNAP greatly reduces the number and cost of local alignment checks performed through several measures: it uses longer seeds to reduce the false positive locations considered, leverages larger memory capacities to speed index lookup, and excludes most candidate locations without fully computing their edit distance to the read. The result is an algorithm that scales well for reads from one hundred to thousands of bases long and provides a rich error model that can match classes of mutations (e.g., longer indels) that today's fast aligners ignore. We calculate that SNAP can align a dataset with 30x coverage of a human genome in less than an hour for a cost of $2 on Amazon EC2, with higher accuracy than BWA. Finally, we describe ongoing work to further improve SNAP.

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"
    - "asap accelerated short read alignment on programmable hardware"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "extending tensorflow s semantics with pipelined execution"):
  with members:
    - "dynamic control flow in large scale machine learning"
    - "in datacenter performance analysis of a tensor processing unit"
    - "execution templates caching control plane decisions for strong scaling of data analytics"
    - "a survey on deep learning in medical image analysis"
    - "big data spark solution for functional magnetic resonance imaging"
    - "improved techniques for training gans"
    - "scalable fast cloud computing with execution templates"
    - "petascale computational systems"

Connected through hyperedge HE-3 (induced by source paper "parallel approach to sliding window sums"):
  with members:
    - "kmc 2 fast and resource frugal k mer counting"

Connected through hyperedge HE-4 (induced by source paper "fast and simple algorithms for computing both lcs_ k and lcs_ k"):
  with members:
    - "longest common subsequence in at least k length order isomorphic substrings"
    - "lcsk practical similarity metric for long strings"
    - "efficient algorithms for the longest common subsequence in k length substrings"
    - "plagiarism detection using rouge and wordnet"

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.DS(Data Structures and Algorithms)