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: "flash randomized algorithms accelerated over cpu gpu for ultra high dimensional similarity search"
  Abstract: We present FLASH ({\bf F}ast {\bf L}SH {\bf A}lgorithm for {\bf S}imilarity search accelerated with {\bf H}PC (High-Performance Computing)), a similarity search system for ultra-high dimensional datasets on a single machine, which does not require similarity computation. Our system is an auspicious illustration of the power of randomized algorithms carefully tailored for high-performance computing platforms. We leverage LSH style randomized indexing procedure and combine it with several principled techniques, such as reservoir sampling, recent advances in one-pass minwise hashing, and count based estimations. The combination, while retaining sound theoretical guarantees, reduces the computational as well as parallelization overhead of our proposal. We provide CPU and hybrid CPU-GPU implementations of FLASH for replicability of our results this https URL#R##N#We evaluate FLASH on several real high dimensional datasets coming from different domains including text, malicious URL, click-through prediction, social networks, etc. Our experiments shed new light on the difficulties associated with datasets having several millions of dimensions. Current state-of-the-art implementations either fail on the presented scale or are orders of magnitude slower than our system. FLASH is capable of computing an approximate k-NN graph, from scratch, over full webspam dataset (1.3 billion nonzeros) in less than 10 seconds. Computing full k-NN graph in less than 10 seconds on webspam dataset, using brute-force ($n^2D$), will require at least 20 TFLOPS. We hope that FLASH gets adopted in practice.

Connected through hyperedge HE-1 (induced by source paper "approximate similarity search under edit distance using locality sensitive hashing"):
  with members:
    - "constant factor approximation of near linear edit distance in near linear time"
    - "race sub linear memory sketches for approximate near neighbor search on streaming data"
    - "near linear time insertion deletion codes and 1 e approximating edit distance via indexing"
    - "constant factor approximations to edit distance on far input pairs in nearly linear time"
    - "approximate nearest neighbor search in high dimensions"
    - "set similarity search for skewed data"
    - "hardness of approximate nearest neighbor search"
    - "fast entropy bounded string dictionary look up with mismatches"

Connected through hyperedge HE-2 (induced by source paper "slide in defense of smart algorithms over hardware acceleration for large scale deep learning systems"):
  with members:
    - "lsh sampling breaks the computation chicken and egg loop in adaptive stochastic gradient estimation"
    - "data curation with deep learning vision towards self driving data curation"
    - "auto tuning tensorflow threading model for cpu backend"
    - "scaling up split merge mcmc with locality sensitive sampling lss"
    - "in datacenter performance analysis of a tensor processing unit"
    - "adaptive sampled softmax with kernel based sampling"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "database meets deep learning challenges and opportunities"

Connected through hyperedge HE-3 (induced by source paper "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"):
  with members:
    - "billion scale similarity search with gpus"
    - "graph based time space trade offs for approximate near neighbors"
    - "parameter free locality sensitive hashing for spherical range reporting"
    - "scalable and robust set similarity join"
    - "hybrid lsh faster near neighbors reporting in high dimensional space"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "off the beaten path let s replace term based retrieval with k nn search"
    - "practical and optimal lsh for angular distance"

Connected through hyperedge HE-4 (induced by source paper "index based high dimensional cosine threshold querying with optimality guarantees"):
  with members:
    - "hd index pushing the scalability accuracy boundary for approximate knn search in high dimensional spaces"
    - "on the complexity of inner product similarity join"
    - "cosine similarity search with multi index hashing"
    - "practical and optimal lsh for angular distance"
    - "fast exact max kernel search"
    - "optimal aggregation algorithms for middleware"

Connected through hyperedge HE-5 (induced by source paper "speeding up the verification phase of set similarity joins in the gpgpu paradigm"):
  with members:
    - "billion scale similarity search with gpus"
    - "a dynamic hash table for the gpu"
    - "efficient large scale approximate nearest neighbor search on the gpu"
    - "a generic inverted index framework for similarity search on the gpu technical report"
    - "v smart join a scalable mapreduce framework for all pair similarity joins of multisets and vectors"

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)