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: "probabilistic polynomials and hamming nearest neighbors"
  Abstract: We show how to compute any symmetric Boolean function on n variables over any field (as well as the integers) with a probabilistic polynomial of degree O(a#x221A;n log(1/a#x03B5;))) and error at most a#x03B5;. The degree dependence on n and a#x03B5; is optimal, matching a lower bound of Razborov (1987) and Smolensky (1987) for the MAJORITY function. The proof is constructive: a low-degree polynomial can be efficiently sampled from the distribution. This polynomial construction is combined with other algebraic ideas to give the first sub quadratic time algorithm for computing a (worst-case) batch of Hamming distances in super logarithmic dimensions, exactly. To illustrate, let c(n): N -> N. Suppose we are given a database D of n vectors in {0, 1} (c(n) log n) and a collection of n query vectors Q in the same dimension. For all u in Q, we wish to compute a v in D with minimum Hamming distance from u. We solve this problem in n(2-1/O(c(n) log2 c(n))) randomized time. Hence, the problem is in "truly sub quadratic" time for O(log n) dimensions, and in sub quadratic time for d = o((log2 n)/(log log n)2). We apply the algorithm to computing pairs with maximum inner product, closest pair in l1 for vectors with bounded integer entries, and pairs with maximum Jaccard coefficients.

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 "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-3 (induced by source paper "fair near neighbor search independent range sampling in high dimensions"):
  with members:
    - "independent range sampling revisited again"
    - "approximate nearest neighbor search in high dimensions"
    - "hashing based estimators for kernel density in high dimensions"
    - "parameter free locality sensitive hashing for spherical range reporting"
    - "sub linear privacy preserving near neighbor search with untrusted server on large scale datasets"
    - "equality of opportunity in supervised learning"
    - "sub linear privacy preserving search with untrusted server and semi honest parties"
    - "optimal hashing based time space trade offs for approximate near neighbors"

Connected through hyperedge HE-4 (induced by source paper "the role of local intrinsic dimensionality in benchmarking nearest neighbor search"):
  with members:
    - "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"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "efficient estimation of word representations in vector space"
    - "mlpack a scalable c machine learning library"

Connected through hyperedge HE-5 (induced by source paper "on the probabilistic degrees of symmetric boolean functions"):
  with members:
    - "on the probabilistic degree of or over the reals"
    - "on polynomial approximations to ac 0"
    - "analysis of boolean functions"

Connected through hyperedge HE-6 (induced by source paper "on the hardness of approximate and exact bichromatic maximum inner product"):
  with members:
    - "hardness of approximate nearest neighbor search"
    - "conditional lower bounds for all pairs max flow"
    - "conditional lower bounds for space time tradeoffs"
    - "distributed pcp theorems for hardness of approximation in p"
    - "on the parameterized complexity of approximating dominating set"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "on the complexity of closest pair via polar pair of point sets"
    - "a framework for similarity search with space time tradeoffs using locality sensitive filtering"

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"
    - "embedjoin efficient edit similarity joins via embeddings"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "hardness of approximate nearest neighbor search"):
  with members:
    - "on the hardness of approximate and exact bichromatic maximum inner product"
    - "distributed pcp theorems for hardness of approximation in p"
    - "optimal las vegas locality sensitive data structures"
    - "randomized approximate nearest neighbor search with limited adaptivity"
    - "the curse of medium dimension for geometric problems in almost every norm"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "a faster subquadratic algorithm for finding outlier correlations"

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)