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: "embedjoin efficient edit similarity joins via embeddings"
  Abstract: We study the problem of edit similarity joins, where given a set of strings and a threshold value $K$, we need to output all the pairs of strings whose edit distances are at most $K$. Edit similarity join is a fundamental operation in numerous applications such as data cleaning/integration, bioinformatics, natural language processing, and has been identified as a primitive operator for database systems. This problem has been studied extensively in the literature. However, we observed that all the existing algorithms fall short on long strings and large distance thresholds. #R##N#In this paper we propose an algorithm named \ebdjoin\ that scales very well with string length and distance threshold. Our algorithm is built on the recent advance of metric embeddings for edit distance, and is very different from all of the previous approaches. We demonstrate via an extensive set of experiments that \ebdjoin\ significantly outperforms the previous best algorithms on long strings and large distance thresholds. For example, on a collection of 20,000 real-world DNA sequences each of length 20,000 and a distance threshold that is 1\% of the string length (1\% errors), the previous best algorithms that we have tested cannot finish in 10 hours, while \ebdjoin\ finished in less than 6 minutes. Moreover, \ebdjoin\ scales very well up to 20\% errors which is critical in applications such as bioinformatics, and is far beyond the reach of existing algorithms.

Connected through hyperedge HE-1 (induced by source paper "monotonic cardinality estimation of similarity selection a deep learning approach"):
  with members:
    - "neo a learned query optimizer"
    - "selectivity estimation with deep likelihood models"
    - "sentence bert sentence embeddings using siamese bert networks"
    - "an empirical analysis of deep learning for cardinality estimation"
    - "multi attribute selectivity estimation using deep learning"
    - "an end to end learning based cost estimator"
    - "learned cardinalities estimating correlated joins with deep learning"
    - "learning state representations for query optimization with deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "edit distance embedding using convolutional neural networks"):
  with members:
    - "in defense of the triplet loss for person re identification"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "practical and optimal lsh for angular distance"
    - "edit distance cannot be computed in strongly subquadratic time unless seth is false"
    - "hashing for similarity search a survey"
    - "pass join a partition based method for similarity joins"

Connected through hyperedge HE-3 (induced by source paper "algorithms for similarity search and pseudorandomness"):
  with members:
    - "hardness of approximate nearest neighbor search"
    - "fast locality sensitive hashing for approximate near neighbor search"
    - "fast similarity sketching"
    - "optimal densification for fast and accurate minwise hashing"
    - "parameter free locality sensitive hashing for spherical range reporting"
    - "scalable and robust set similarity join"
    - "fast cross polytope locality sensitive hashing"
    - "a framework for similarity search with space time tradeoffs using locality sensitive filtering"

Connected through hyperedge HE-4 (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-5 (induced by source paper "learning space partitions for nearest neighbor search"):
  with members:
    - "a model for learned bloom filters and optimizing by sandwiching"
    - "sanns scaling up secure approximate k nearest neighbors search"
    - "approximate nearest neighbor search in high dimensions"
    - "competitive caching with machine learned advice"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "the case for learned index structures"
    - "billion scale similarity search with gpus"
    - "learning combinatorial optimization algorithms over graphs"

Connected through hyperedge HE-6 (induced by source paper "similarity problems in high dimensions"):
  with members:
    - "approximate furthest neighbor with application to annulus query"
    - "distributed pcp theorems for hardness of approximation in p"
    - "parameter free locality sensitive hashing for spherical range reporting"
    - "a framework for similarity search with space time tradeoffs using locality sensitive filtering"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "set similarity search beyond minhash"
    - "distance sensitive bloom filters without false negatives"
    - "fast and powerful hashing using tabulation"

Connected through hyperedge HE-7 (induced by source paper "set similarity search for skewed data"):
  with members:
    - "distributed pcp theorems for hardness of approximation in p"
    - "scalable and robust set similarity join"
    - "a framework for similarity search with space time tradeoffs using locality sensitive filtering"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "on the complexity of inner product similarity join"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "explicit correlation amplifiers for finding outlier correlations in deterministic subquadratic time"
    - "set similarity search beyond minhash"

Connected through hyperedge HE-8 (induced by source paper "space efficient feature maps for string alignment kernels"):
  with members:
    - "random warping series a random features method for time series embedding"
    - "ups and downs modeling the visual evolution of fashion trends with one class collaborative filtering"
    - "orthogonal random features"
    - "linearized gmm kernels and normalized random fourier features"
    - "image based recommendations on styles and substitutes"
    - "fastfood approximate kernel expansions in loglinear time"
    - "a kernel for time series based on global alignments"

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)