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: "bottom k and priority sampling set similarity and subset sums with minimal independence"
  Abstract: We consider bottom-k sampling for a set X, picking a sample S_k(X) consisting of the k elements that are smallest according to a given hash function h. With this sample we can estimate the relative size f=|Y|/|X| of any subset Y as |S_k(X) intersect Y|/k. A standard application is the estimation of the Jaccard similarity f=|A intersect B|/|A union B| between sets A and B. Given the bottom-k samples from A and B, we construct the bottom-k sample of their union as S_k(A union B)=S_k(S_k(A) union S_k(B)), and then the similarity is estimated as |S_k(A union B) intersect S_k(A) intersect S_k(B)|/k. #R##N#We show here that even if the hash function is only 2-independent, the expected relative error is O(1/sqrt(fk)). For fk=Omega(1) this is within a constant factor of the expected relative error with truly random hashing. #R##N#For comparison, consider the classic approach of kxmin-wise where we use k hash independent functions h_1,...,h_k, storing the smallest element with each hash function. For kxmin-wise there is an at least constant bias with constant independence, and it is not reduced with larger k. Recently Feigenblat et al. showed that bottom-k circumvents the bias if the hash function is 8-independent and k is sufficiently large. We get down to 2-independence for any k. Our result is based on a simply union bound, transferring generic concentration bounds for the hashing scheme to the bottom-k sample, e.g., getting stronger probability error bounds with higher independence. #R##N#For weighted sets, we consider priority sampling which adapts efficiently to the concrete input weights, e.g., benefiting strongly from heavy-tailed input. This time, the analysis is much more involved, but again we show that generic concentration bounds can be applied.

Connected through hyperedge HE-1 (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"
    - "embedjoin efficient edit similarity joins via embeddings"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "hillview a trillion cell spreadsheet for big data"):
  with members:
    - "big data visualization tools"
    - "foresight recommending visual insights"
    - "progressive analytics a computation paradigm for exploratory data analysis"
    - "visualization aware sampling for very large databases"
    - "a hierarchical aggregation framework for efficient multilevel visual exploration and analysis"
    - "rapid sampling for visualizations with ordering guarantees"
    - "a tutorial on principal component analysis"
    - "blinkdb queries with bounded errors and bounded response times on very large data"

Connected through hyperedge HE-3 (induced by source paper "fast hashing with strong concentration bounds"):
  with members:
    - "optimal streaming and tracking distinct elements with high probability"
    - "faster 64 bit universal hashing using carry less multiplications"
    - "pseudorandomness via the discrete fourier transform"
    - "high speed hashing for integers and strings"
    - "from independence to expansion and back again"
    - "generating k independent variables in constant time"
    - "hashing for statistics over k partitions"
    - "simple tabulation fast expanders double tabulation and high independence"

Connected through hyperedge HE-4 (induced by source paper "fast similarity sketching"):
  with members:
    - "optimal densification for fast and accurate minwise hashing"
    - "set similarity search beyond minhash"
    - "optimal data dependent hashing for approximate near neighbors"
    - "hashing for statistics over k partitions"
    - "feature hashing for large scale multitask learning"

Connected through hyperedge HE-5 (induced by source paper "hyperminhash jaccard index sketching in loglog space"):
  with members:
    - "new cardinality estimation algorithms for hyperloglog sketches"
    - "new cardinality estimation methods for hyperloglog sketches"
    - "fast pseudo random fingerprints"
    - "b bit minwise hashing"

Connected through hyperedge HE-6 (induced by source paper "hyperminhash minhash in loglog space"):
  with members:
    - "new cardinality estimation algorithms for hyperloglog sketches"
    - "new cardinality estimation methods for hyperloglog sketches"
    - "fast pseudo random fingerprints"

Connected through hyperedge HE-7 (induced by source paper "range efficient consistent sampling and locality sensitive hashing for polygons"):
  with members:
    - "consistent weighted sampling made fast small and easy"
    - "approximately minwise independence with twisted tabulation"

Connected through hyperedge HE-8 (induced by source paper "set similarity search beyond minhash"):
  with members:
    - "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"
    - "tight lower bounds for data dependent locality sensitive hashing"
    - "practical and optimal lsh for angular distance"
    - "tradeoffs for nearest neighbors on the sphere"
    - "optimal data dependent hashing for approximate near neighbors"
    - "beyond locality sensitive hashing"

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)