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: "bayesian locality sensitive hashing for fast similarity search"
  Abstract: Given a collection of objects and an associated similarity measure, the all-pairs similarity search problem asks us to find all pairs of objects with similarity greater than a certain user-specified threshold. Locality-sensitive hashing (LSH) based methods have become a very popular approach for this problem. However, most such methods only use LSH for the first phase of similarity search - i.e. efficient indexing for candidate generation. In this paper, we present BayesLSH, a principled Bayesian algorithm for the subsequent phase of similarity search - performing candidate pruning and similarity estimation using LSH. A simpler variant, BayesLSH-Lite, which calculates similarities exactly, is also presented. BayesLSH is able to quickly prune away a large majority of the false positive candidate pairs, leading to significant speedups over baseline approaches. For BayesLSH, we also provide probabilistic guarantees on the quality of the output, both in terms of accuracy and recall. Finally, the quality of BayesLSH's output can be easily tuned and does not require any manual setting of the number of hashes to use for similarity estimation, unlike standard approaches. For two state-of-the-art candidate generation algorithms, AllPairs and LSH, BayesLSH enables significant speedups, typically in the range 2x-20x for a wide variety of datasets.

Connected through hyperedge HE-1 (induced by source paper "blocking and filtering techniques for entity resolution a survey"):
  with members:
    - "schema agnostic progressive entity resolution"
    - "pigeonring a principle for faster thresholded similarity search"
    - "scalable and robust set similarity join"
    - "set similarity search beyond minhash"
    - "a dnf blocking scheme learner for heterogeneous datasets"
    - "hashing for similarity search a survey"
    - "a comparison of blocking methods for record linkage"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-2 (induced by source paper "end to end entity resolution for big data a survey"):
  with members:
    - "schema agnostic progressive entity resolution"
    - "a survey of blocking and filtering techniques for entity resolution"
    - "minoaner schema agnostic non iterative massively parallel resolution of web entities"
    - "pigeonring a principle for faster thresholded similarity search"
    - "reuse and adaptation for entity resolution through transfer learning"
    - "snorkel rapid training data creation with weak supervision"
    - "distributed holistic clustering on linked data"
    - "a dnf blocking scheme learner for heterogeneous datasets"

Connected through hyperedge HE-3 (induced by source paper "the space complexity of inner product filters"):
  with members:
    - "norm ranging lsh for maximum inner product search"
    - "approximate nearest neighbors in limited space"
    - "adaptive mapreduce similarity joins"
    - "practical data dependent metric compression with provable guarantees"
    - "distributed pcp theorems for hardness of approximation in p"
    - "flexpoint an adaptive numerical format for efficient training of deep neural networks"
    - "bolt accelerated data mining with fast vector compression"
    - "scalable and robust set similarity join"

Connected through hyperedge HE-4 (induced by source paper "a survey of blocking and filtering techniques for entity resolution"):
  with members:
    - "schema agnostic progressive entity resolution"
    - "reuse and adaptation for entity resolution through transfer learning"
    - "scalable and robust set similarity join"
    - "set similarity search beyond minhash"
    - "a dnf blocking scheme learner for heterogeneous datasets"
    - "hashing for similarity search a survey"
    - "a comparison of blocking methods for record linkage"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "puffinn parameterless and universally fast finding of nearest neighbors"):
  with members:
    - "confirmation sampling for exact nearest neighbor search"
    - "optimization of indexing based on k nearest neighbor graph for proximity search in high dimensional data"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "billion scale similarity search with gpus"
    - "fast locality sensitive hashing for approximate near neighbor search"
    - "fast similarity sketching"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "practical and optimal lsh for angular distance"

Connected through hyperedge HE-6 (induced by source paper "pigeonring a principle for faster thresholded similarity search"):
  with members:
    - "scalable and robust set similarity join"
    - "set similarity search beyond minhash"
    - "practical and optimal lsh for angular distance"
    - "hashing for similarity search a survey"
    - "alae accelerating local alignment with affine gap exactly in biosequence databases"
    - "searching in one billion vectors re rank with source coding"
    - "pass join a partition based method for similarity joins"

Connected through hyperedge HE-7 (induced by source paper "chasing similarity distribution aware aggregation scheduling extended version"):
  with members:
    - "moment based quantile sketches for efficient high cardinality aggregation queries"
    - "parameter hub a rack scale parameter server for distributed deep neural network training"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "reductions between expansion problems"

Connected through hyperedge HE-8 (induced by source paper "a review for weighted minhash algorithms"):
  with members:
    - "improved consistent weighted sampling revisited"
    - "consistent weighted sampling made fast small and easy"

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.DB(Databases)