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: "lsh ensemble internet scale domain search"
  Abstract: We study the problem of domain search where a domain is a set of distinct values from an unspecified universe. We use Jaccard set containment, defined as $|Q \cap X|/|Q|$, as the relevance measure of a domain $X$ to a query domain $Q$. Our choice of Jaccard set containment over Jaccard similarity makes our work particularly suitable for searching Open Data and data on the web, as Jaccard similarity is known to have poor performance over sets with large differences in their domain sizes. We demonstrate that the domains found in several real-life Open Data and web data repositories show a power-law distribution over their domain sizes. #R##N#We present a new index structure, Locality Sensitive Hashing (LSH) Ensemble, that solves the domain search problem using set containment at Internet scale. Our index structure and search algorithm cope with the data volume and skew by means of data sketches (MinHash) and domain partitioning. Our index structure does not assume a prescribed set of values. We construct a cost model that describes the accuracy of LSH Ensemble with any given partitioning. This allows us to formulate the partitioning for LSH Ensemble as an optimization problem. We prove that there exists an optimal partitioning for any distribution. Furthermore, for datasets following a power-law distribution, as observed in Open Data and Web data corpora, we show that the optimal partitioning can be approximated using equi-depth, making it efficient to use in practice. #R##N#We evaluate our algorithm using real data (Canadian Open Data and WDC Web Tables) containing up over 262 M domains. The experiments demonstrate that our index consistently outperforms other leading alternatives in accuracy and performance. The improvements are most dramatic for data with large skew in the domain sizes. Even at 262 M domains, our index sustains query performance with under 3 seconds response time.

Connected through hyperedge HE-1 (induced by source paper "lsf join locality sensitive filtering for distributed all pairs set similarity under skew"):
  with members:
    - "hardness of bichromatic closest pair with jaccard similarity"
    - "set similarity search for skewed data"
    - "adaptive mapreduce similarity joins"
    - "when hashes met wedges a distributed algorithm for finding high similarity vectors"
    - "scalable and robust set similarity join"
    - "a framework for similarity search with space time tradeoffs using locality sensitive filtering"
    - "on the complexity of inner product similarity join"
    - "massively parallel similarity join edge isoperimetry and distance correlations on the hypercube"

Connected through hyperedge HE-2 (induced by source paper "iclab a global longitudinal internet censorship measurement platform"):
  with members:
    - "on identifying anomalies in tor usage with applications in detecting internet censorship"
    - "tranco a research oriented top sites ranking hardened against manipulation"
    - "exploring server side blocking of regions"
    - "an empirical study of the i2p anonymity network and its censorship resistance"
    - "a long way to the top significance structure and stability of internet top lists"
    - "a bestiary of blocking the motivations and modes behind website unavailability"
    - "where the light gets in analyzing web censorship mechanisms in india"
    - "filteredweb a framework for the automated search based discovery of blocked urls"

Connected through hyperedge HE-3 (induced by source paper "data lake organization"):
  with members:
    - "sherlock a deep learning approach to semantic data type detection"
    - "xgboost a scalable tree boosting system"
    - "bag of tricks for efficient text classification"
    - "distributed representations of words and phrases and their compositionality"
    - "being bayesian about network structure"
    - "answering table queries on the web using column keywords"

Connected through hyperedge HE-4 (induced by source paper "gb kmv an augmented kmv sketch for approximate containment similarity search"):
  with members:
    - "silkmoth an efficient method for finding related sets with maximum matching constraints"
    - "fast similarity sketching"
    - "set containment join revisited"
    - "set similarity search beyond minhash"
    - "a minimal variance estimator for the cardinality of big data set intersection"

Connected through hyperedge HE-5 (induced by source paper "optimizing organizations for navigating data lakes"):
  with members:
    - "distributed representations of words and phrases and their compositionality"
    - "answering table queries on the web using column keywords"

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)