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: "hd index pushing the scalability accuracy boundary for approximate knn search in high dimensional spaces"
  Abstract: Nearest neighbor searching of large databases in high-dimensional spaces is inherently difficult due to the curse of dimensionality. A flavor of approximation is, therefore, necessary to practically solve the problem of nearest neighbor search. In this paper, we propose a novel yet simple indexing scheme, HD-Index, to solve the problem of approximate k-nearest neighbor queries in massive high-dimensional databases. HD-Index consists of a set of novel hierarchical structures called RDB-trees built on Hilbert keys of database objects. The leaves of the RDB-trees store distances of database objects to reference objects, thereby allowing efficient pruning using distance filters. In addition to triangular inequality, we also use Ptolemaic inequality to produce better lower bounds. Experiments on massive (up to billion scale) high-dimensional (up to 1000+) datasets show that HD-Index is effective, efficient, and scalable.

Connected through hyperedge HE-1 (induced by source paper "mmlsh a practical and efficient technique for processing approximate nearest neighbor queries on multimedia data"):
  with members:
    - "qwlsh cache conscious indexing for processing similarity search query workloads in high dimensional spaces"

Connected through hyperedge HE-2 (induced by source paper "drawbacks and proposed solutions for real time processing on existing state of the art locality sensitive hashing techniques"):
  with members:
    - "a survey on learning to hash"
    - "sift meets cnn a decade survey of instance retrieval"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-3 (induced by source paper "satellite system graph towards the efficiency up boundary of graph based approximate nearest neighbor search"):
  with members:
    - "billion scale similarity search with gpus"
    - "efanna an extremely fast approximate nearest neighbor search algorithm based on knn graph"
    - "efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs"

Connected through hyperedge HE-4 (induced by source paper "index based high dimensional cosine threshold querying with optimality guarantees"):
  with members:
    - "flash randomized algorithms accelerated over cpu gpu for ultra high dimensional similarity search"
    - "on the complexity of inner product similarity join"
    - "cosine similarity search with multi index hashing"
    - "practical and optimal lsh for angular distance"
    - "fast exact max kernel search"
    - "optimal aggregation algorithms for middleware"

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.DB(Databases)