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: "approximate furthest neighbor with application to annulus query"
  Abstract: Much recent work has been devoted to approximate nearest neighbor queries. Motivated by applications in recommender systems, we consider approximate furthest neighbor (AFN) queries and present a simple, fast, and highly practical data structure for answering AFN queries in high-dimensional Euclidean space. The method builds on the technique of Indyk (SODA 2003), storing random projections to provide sublinear query time for AFN. However, we introduce a different query algorithm, improving on Indyk's approximation factor and reducing the running time by a logarithmic factor. We also present a variation based on a query-independent ordering of the database points; while this does not have the provable approximation factor of the query-dependent data structure, it offers significant improvement in time and space complexity. We give a theoretical analysis and experimental results. As an application, the query-dependent approach is used for deriving a data structure for the approximate annulus query problem, which is defined as follows: given an input set S and two parameters r 0 and w ź 1 , construct a data structure that returns for each query point q a point p ź S such that the distance between p and q is at least r / w and at most wr . HighlightsPresents the approximate furthest neighbor problem (AFN).Gives sub-linear time solutions to AFN based on random projections.Presents the approximate annulus query problem (AAQ).Gives a sub-linear time solution to AAQ based on random projections and locality sensitive hashing.

Connected through hyperedge HE-1 (induced by source paper "similarity problems in high dimensions"):
  with members:
    - "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"
    - "set similarity search beyond minhash"
    - "distance sensitive bloom filters without false negatives"
    - "fast and powerful hashing using tabulation"

Connected through hyperedge HE-2 (induced by source paper "exquisitor interactive learning at large"):
  with members:
    - "revisiting the inverted indices for billion scale approximate nearest neighbors"
    - "cost effective active learning for deep image classification"
    - "active decision boundary annotation with deep generative models"
    - "a survey on learning to hash"
    - "yfcc100m the new data in multimedia research"
    - "whittlesearch interactive image search with relative attribute feedback"
    - "going deeper with convolutions"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-3 (induced by source paper "probabilistic smallest enclosing ball in high dimensions via subgradient sampling"):
  with members:
    - "stochastic k center and j flat center problems"
    - "geometric median in nearly linear time"

Connected through hyperedge HE-4 (induced by source paper "distance sensitive hashing"):
  with members:
    - "embedjoin efficient edit similarity joins via embeddings"
    - "parameter free locality sensitive hashing for spherical range reporting"
    - "sub linear privacy preserving near neighbor search with untrusted server on large scale datasets"
    - "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"
    - "set similarity search beyond minhash"
    - "tight lower bounds for data dependent 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)