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: "set similarity search beyond minhash"
  Abstract: We consider the problem of approximate set similarity search under Braun-Blanquet similarity $B(\mathbf{x}, \mathbf{y}) = |\mathbf{x} \cap \mathbf{y}| / \max(|\mathbf{x}|, |\mathbf{y}|)$. The $(b_2, b_2)$-approximate Braun-Blanquet similarity search problem is to preprocess a collection of sets $P$ such that, given a query set $\mathbf{q}$, if there exists $\mathbf{x} \in P$ with $B(\mathbf{q}, \mathbf{x}) \geq b_1$, then we can efficiently return $\mathbf{x}' \in P$ with $B(\mathbf{q}, \mathbf{x}') > b_2$. #R##N#We present a simple data structure that solves this problem with space usage $O(n^{1+\rho}\log n + \sum_{\mathbf{x} \in P}|\mathbf{x}|)$ and query time $O(|\mathbf{q}|n^{\rho} \log n)$ where $n = |P|$ and $\rho = \log(1/b_1)/\log(1/b_2)$. Making use of existing lower bounds for locality-sensitive hashing by O'Donnell et al. (TOCT 2014) we show that this value of $\rho$ is tight across the parameter space, i.e., for every choice of constants $0 < b_2 < b_1 < 1$. #R##N#In the case where all sets have the same size our solution strictly improves upon the value of $\rho$ that can be obtained through the use of state-of-the-art data-independent techniques in the Indyk-Motwani locality-sensitive hashing framework (STOC 1998) such as Broder's MinHash (CCS 1997) for Jaccard similarity and Andoni et al.'s cross-polytope LSH (NIPS 2015) for cosine similarity. Surprisingly, even though our solution is data-independent, for a large part of the parameter space we outperform the currently best data-dependent method by Andoni and Razenshteyn (STOC 2015).

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"
    - "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"
    - "scaling multiple source entity resolution using statistically efficient transfer learning"

Connected through hyperedge HE-2 (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-3 (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-4 (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"
    - "fast similarity sketching"
    - "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"

Connected through hyperedge HE-5 (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"
    - "embedjoin efficient edit similarity joins via embeddings"
    - "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"
    - "distance sensitive bloom filters without false negatives"
    - "fast and powerful hashing using tabulation"

Connected through hyperedge HE-6 (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"
    - "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"
    - "scaling multiple source entity resolution using statistically efficient transfer learning"

Connected through hyperedge HE-7 (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"
    - "fast similarity sketching"
    - "a survey on learning to hash"
    - "exact weighted minwise hashing in constant time"
    - "robust 1 bit compressive sensing via binary stable embeddings of sparse vectors"
    - "b bit minwise hashing"

Connected through hyperedge HE-8 (induced by source paper "hardness of bichromatic closest pair with jaccard similarity"):
  with members:
    - "an equivalence class for orthogonal vectors"
    - "hardness of approximate nearest neighbor search"
    - "on the hardness of approximate and exact bichromatic maximum inner product"

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)