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: "fast similarity sketching"
  Abstract: We consider the Similarity Sketching problem: Given a universe $[u]= \{0,\ldots,u-1\}$ we want a random function $S$ mapping subsets $A\subseteq [u]$ into vectors $S(A)$ of size $t$, such that similarity is preserved. More precisely: Given sets $A,B\subseteq [u]$, define $X_i=[S(A)[i]= S(B)[i]]$ and $X=\sum_{i\in [t]}X_i$. We want to have $E[X]=t\cdot J(A,B)$, where $J(A,B)=|A\cap B|/|A\cup B|$ and furthermore to have strong concentration guarantees (i.e. Chernoff-style bounds) for $X$. This is a fundamental problem which has found numerous applications in data mining, large-scale classification, computer vision, similarity search, etc. via the classic MinHash algorithm. The vectors $S(A)$ are also called sketches. #R##N#The seminal $t\times$MinHash algorithm uses $t$ random hash functions $h_1,\ldots, h_t$, and stores $\left(\min_{a\in A}h_1(A),\ldots, \min_{a\in A}h_t(A)\right)$ as the sketch of $A$. The main drawback of MinHash is, however, its $O(t\cdot |A|)$ running time, and finding a sketch with similar properties and faster running time has been the subject of several papers. Addressing this, Li et al. [NIPS'12] introduced one permutation hashing (OPH), which creates a sketch of size $t$ in $O(t + |A|)$ time, but with the drawback that possibly some of the $t$ entries are "empty" when $|A| = O(t)$. One could argue that sketching is not necessary in this case, however the desire in most applications is to have one sketching procedure that works for sets of all sizes. Therefore, filling out these empty entries is the subject of several follow-up papers initiated by Shrivastava and Li [ICML'14]. However, these "densification" schemes fail to provide good concentration bounds exactly in the case $|A| = O(t)$, where they are needed. (continued...)

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"
    - "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"
    - "a framework for similarity search with space time tradeoffs using locality sensitive filtering"

Connected through hyperedge HE-2 (induced by source paper "subsets and supermajorities unifying hashing based set similarity search"):
  with members:
    - "norm ranging lsh for maximum inner product search"
    - "an equivalence class for orthogonal vectors"
    - "hardness of approximate nearest neighbor search"
    - "distributed pcp theorems for hardness of approximation in p"
    - "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"
    - "set similarity search beyond minhash"

Connected through hyperedge HE-3 (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"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "practical and optimal lsh for angular distance"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-4 (induced by source paper "analysis of sparsehash an efficient embedding of set similarity via sparse projections"):
  with members:
    - "optimizing bloom filter challenges solutions and comparisons"
    - "binary adaptive embeddings from order statistics of random projections"
    - "a survey on learning to hash"
    - "exact weighted minwise hashing in constant time"
    - "set similarity search beyond minhash"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "b bit minwise hashing"

Connected through hyperedge HE-5 (induced by source paper "probminhash a class of locality sensitive hash algorithms for the probability jaccard similarity"):
  with members:
    - "fast random integer generation in an interval"
    - "bagminhash minwise hashing algorithm for weighted sets"
    - "maximally consistent sampling and the jaccard index of probability distributions"
    - "optimal densification for fast and accurate minwise hashing"
    - "superminhash a new minwise hashing algorithm for jaccard similarity estimation"
    - "b bit minwise hashing"

Connected through hyperedge HE-6 (induced by source paper "bagminhash minwise hashing algorithm for weighted sets"):
  with members:
    - "topic modeling of public repositories at scale using names in source code"
    - "optimal densification for fast and accurate minwise hashing"
    - "improved consistent weighted sampling revisited"
    - "superminhash a new minwise hashing algorithm for jaccard similarity estimation"
    - "ssh sketch shingle hash for indexing massive scale time series"
    - "linearized gmm kernels and normalized random fourier features"
    - "consistent weighted sampling made fast small and easy"
    - "optimal discrete uniform generation from coin flips and applications"

Connected through hyperedge HE-7 (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"
    - "set containment join revisited"
    - "lsh ensemble internet scale domain search"
    - "set similarity search beyond minhash"
    - "a minimal variance estimator for the cardinality of big data set intersection"

Connected through hyperedge HE-8 (induced by source paper "neural distributed autoassociative memories a survey"):
  with members:
    - "autoscaling bloom filter controlling trade off between true and false positives"
    - "dense associative memory is robust to adversarial inputs"
    - "optimal las vegas locality sensitive data structures"
    - "associative memories to accelerate approximate nearest neighbor search"
    - "dense associative memory for pattern recognition"
    - "set similarity search beyond minhash"
    - "distance sensitive bloom filters without false negatives"
    - "representing objects relations and sequences"

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)