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: "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"
  Abstract: This paper describes ANN-Benchmarks, a tool for evaluating the performance of in-memory approximate nearest neighbor algorithms. It provides a standard interface for measuring the performance and quality achieved by nearest neighbor algorithms on different standard data sets. It supports several different ways of integrating $k$-NN algorithms, and its configuration system automatically tests a range of parameter settings for each algorithm. Algorithms are compared with respect to many different (approximate) quality measures, and adding more is easy and fast; the included plotting front-ends can visualise these as images, $\LaTeX$ plots, and websites with interactive plots. ANN-Benchmarks aims to provide a constantly updated overview of the current state of the art of $k$-NN algorithms. In the short term, this overview allows users to choose the correct $k$-NN algorithm and parameters for their similarity search task; in the longer term, algorithm designers will be able to use this overview to test and refine automatic parameter tuning. The paper gives an overview of the system, evaluates the results of the benchmark, and points out directions for future work. Interestingly, very different approaches to $k$-NN search yield comparable quality-performance trade-offs. The system is available at this http URL .

Connected through hyperedge HE-1 (induced by source paper "approximating spectral clustering via sampling a review"):
  with members:
    - "optimized deformed laplacian for spectrum based community detection in sparse heterogeneous graphs"
    - "revisiting the bethe hessian improved community detection in sparse heterogeneous graphs"
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "graph reduction by local variation"
    - "scalable spectral clustering using random binning features"
    - "graph reduction with spectral and cut guarantees"
    - "spectrally approximating large graphs with smaller graphs"
    - "lectures on randomized numerical linear algebra"

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 "learning space partitions for nearest neighbor search"):
  with members:
    - "a model for learned bloom filters and optimizing by sandwiching"
    - "sanns scaling up secure approximate k nearest neighbors search"
    - "approximate nearest neighbor search in high dimensions"
    - "competitive caching with machine learned advice"
    - "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-4 (induced by source paper "situating sentence embedders with nearest neighbor overlap"):
  with members:
    - "optimization of indexing based on k nearest neighbor graph for proximity search in high dimensional data"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "content based citation recommendation"
    - "deep contextualized word representations"
    - "universal sentence encoder"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "senteval an evaluation toolkit for universal sentence representations"

Connected through hyperedge HE-5 (induced by source paper "sanns scaling up secure approximate k nearest neighbors search"):
  with members:
    - "scalable nearest neighbor search for optimal transport"
    - "approximate nearest neighbor search in high dimensions"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "scalable secure computation of statistical functions with applications to k nearest neighbors"
    - "chameleon a hybrid secure computation framework for machine learning applications"
    - "gazelle a low latency framework for secure neural network inference"
    - "secure k ish nearest neighbors classifier"
    - "billion scale similarity search with gpus"

Connected through hyperedge HE-6 (induced by source paper "near neighbor who is the fairest of them all"):
  with members:
    - "fair algorithms for clustering"
    - "scalable fair clustering"
    - "independent range sampling revisited again"
    - "fair near neighbor search independent range sampling in high dimensions"
    - "convex formulations for fair principal component analysis"
    - "fair clustering through fairlets"
    - "fair algorithms for learning in allocation problems"
    - "a reductions approach to fair classification"

Connected through hyperedge HE-7 (induced by source paper "incorporating query term independence assumption for efficient retrieval and ranking using deep neural networks"):
  with members:
    - "passage re ranking with bert"
    - "an updated duet model for passage re ranking"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "neural ranking models with multiple document fields"
    - "benchmark for complex answer retrieval"
    - "a deep relevance matching model for ad hoc retrieval"
    - "learning to match using local and distributed representations of text for web search"

Connected through hyperedge HE-8 (induced by source paper "learning sublinear time indexing for nearest neighbor search"):
  with members:
    - "sanns scaling up secure approximate k nearest neighbors search"
    - "approximate nearest neighbor search in high dimensions"
    - "competitive caching with machine learned advice"
    - "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"

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.IR(Information Retrieval)