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: "practical and optimal lsh for angular distance"
  Abstract: We show the existence of a Locality-Sensitive Hashing (LSH) family for the angular distance that yields an approximate Near Neighbor Search algorithm with the asymptotically optimal running time exponent. Unlike earlier algorithms with this property (e.g., Spherical LSH [Andoni, Indyk, Nguyen, Razenshteyn 2014], [Andoni, Razenshteyn 2015]), our algorithm is also practical, improving upon the well-studied hyperplane LSH [Charikar, 2002] in practice. We also introduce a multiprobe version of this algorithm, and conduct experimental evaluation on real and synthetic data sets. #R##N#We complement the above positive results with a fine-grained lower bound for the quality of any LSH family for angular distance. Our lower bound implies that the above LSH family exhibits a trade-off between evaluation time and quality that is close to optimal for a natural class of LSH functions.

Connected through hyperedge HE-1 (induced by source paper "differentiable reasoning over a virtual knowledge base"):
  with members:
    - "latent retrieval for weakly supervised open domain question answering"
    - "bag bi directional attention entity graph convolutional network for multi hop reasoning question answering"
    - "explore propose and assemble an interpretable model for multi hop reading comprehension"
    - "pullnet open domain question answering with iterative retrieval on knowledge bases and text"
    - "cognitive graph for multi hop reading comprehension at scale"
    - "neural query language a knowledge base query language for tensorflow"
    - "multi hop paragraph retrieval for open domain question answering"
    - "leveraging knowledge bases in lstms for improving machine reading"

Connected through hyperedge HE-2 (induced by source paper "reformer the efficient transformer"):
  with members:
    - "augmenting self attention with persistent memory"
    - "stand alone self attention in vision models"
    - "low memory neural network training a technical report"
    - "generating long sequences with sparse transformers"
    - "large memory layers with product keys"
    - "adaptive attention span in transformers"
    - "generating wikipedia by summarizing long sequences"
    - "scaling neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "edit distance embedding using convolutional neural networks"):
  with members:
    - "in defense of the triplet loss for person re identification"
    - "embedjoin efficient edit similarity joins via embeddings"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"
    - "edit distance cannot be computed in strongly subquadratic time unless seth is false"
    - "hashing for similarity search a survey"
    - "pass join a partition based method for similarity joins"

Connected through hyperedge HE-4 (induced by source paper "segmentation of objects by hashing"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "searching for efficient multi scale architectures for dense image prediction"
    - "masklab instance segmentation by refining object detection with semantic and direction features"
    - "rethinking atrous convolution for semantic image segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-5 (induced by source paper "autoblock a hands off blocking framework for entity matching"):
  with members:
    - "deeper deep entity resolution"
    - "attention is all you need"
    - "enriching word vectors with subword information"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "optimal data dependent hashing for approximate near neighbors"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-6 (induced by source paper "group membership verification with privacy sparse or dense"):
  with members:
    - "privacy preserving group membership verification and identification"
    - "privacy preserving identification using sparse approximation with ambiguization"

Connected through hyperedge HE-7 (induced by source paper "when hashing met matching efficient spatio temporal search for ridesharing"):
  with members:
    - "how to match when all vertices arrive online"

Connected through hyperedge HE-8 (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"

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)