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: "sanns scaling up secure approximate k nearest neighbors search"
  Abstract: We present new secure protocols for approximate $k$-nearest neighbor search ($k$-NNS) over the Euclidean distance in the semi-honest model. Our implementation is able to handle massive datasets efficiently. On the algorithmic front, we show a new circuit for the approximate top-$k$ selection from $n$ numbers that is built from merely $O(n + \mathrm{poly}(k))$ comparators. Using this circuit as a subroutine, we design new approximate $k$-NNS algorithms and two corresponding secure protocols: 1) optimized linear scan; 2) clustering-based sublinear time algorithm. #R##N#Our secure protocols utilize a combination of additively-homomorphic encryption, garbled circuit and Oblivious RAM. Along the way, we introduce various optimizations to these primitives, which drastically improve concrete efficiency. #R##N#We evaluate the new protocols empirically and show that they are able to handle datasets that are significantly larger than in the prior work. For instance, running on two standard Azure instances within the same availability zone, for a dataset of $96$-dimensional descriptors of $10\,000\,000$ images, we can find $10$ nearest neighbors with average accuracy $0.9$ in under $10$ seconds improving upon prior work by at least two orders of magnitude.

Connected through hyperedge HE-1 (induced by source paper "learning space partitions for nearest neighbor search"):
  with members:
    - "a model for learned bloom filters and optimizing by sandwiching"
    - "approximate nearest neighbor search in high dimensions"
    - "competitive caching with machine learned advice"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "the case for learned index structures"
    - "billion scale similarity search with gpus"
    - "learning combinatorial optimization algorithms over graphs"
    - "embedjoin efficient edit similarity joins via embeddings"

Connected through hyperedge HE-2 (induced by source paper "lower bounds for oblivious near neighbor search"):
  with members:
    - "hardness of approximate nearest neighbor search"
    - "scalable secure computation of statistical functions with applications to k nearest neighbors"
    - "lower bounds for oblivious data structures"
    - "distributed pcp theorems for hardness of approximation in p"
    - "crossing the logarithmic barrier for dynamic boolean data structure lower bounds"
    - "new unconditional hardness results for dynamic and online problems"
    - "secure k nearest neighbor query over encrypted data in outsourced environments"
    - "the cell probe complexity of dynamic range counting"

Connected through hyperedge HE-3 (induced by source paper "learning sublinear time indexing for nearest neighbor search"):
  with members:
    - "approximate nearest neighbor search in high dimensions"
    - "competitive caching with machine learned advice"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
    - "billion scale similarity search with gpus"
    - "optimal hashing based time space trade offs for approximate near neighbors"
    - "a deep learning approach to structured signal recovery"
    - "learning to hash for indexing big data a survey"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-4 (induced by source paper "sub linear privacy preserving near neighbor search"):
  with members:
    - "lower bounds for oblivious near neighbor search"
    - "fair near neighbor search independent range sampling in high dimensions"
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "exploring privacy preservation in outsourced k nearest neighbors with multiple data owners"
    - "privacy via the johnson lindenstrauss transform"
    - "coding for random projections"
    - "secure k nearest neighbor query over encrypted data in outsourced environments"
    - "a simple and practical algorithm for differentially private data release"

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)