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: "cosine similarity search with multi index hashing"
  Abstract: Due to rapid development of the Internet, recent years have witnessed an explosion in the rate of data generation. Dealing with data at current scales brings up unprecedented challenges. From the algorithmic view point, executing existing linear algorithms in information retrieval and machine learning on such tremendous amounts of data incur intolerable computational and storage costs. To address this issue, there is a growing interest to map data points in large-scale datasets to binary codes. This can significantly reduce the storage complexity of large-scale datasets. However, one of the most compelling reasons for using binary codes or any discrete representation is that they can be used as direct indices into a hash table. Incorporating hash table offers fast query execution; one can look up the nearby buckets in a hash table populated with binary codes to retrieve similar items. Nonetheless, if binary codes are compared in terms of the cosine similarity rather than the Hamming distance, there is no fast exact sequential procedure to find the $K$ closest items to the query other than the exhaustive search. Given a large dataset of binary codes and a binary query, the problem that we address is to efficiently find $K$ closest codes in the dataset that yield the largest cosine similarities to the query. To handle this issue, we first elaborate on the relation between the Hamming distance and the cosine similarity. This allows finding the sequence of buckets to check in the hash table. Having this sequence, we propose a multi-index hashing approach that can increase the search speed up to orders of magnitude in comparison to the exhaustive search and even approximation methods such as LSH. We empirically evaluate the performance of the proposed algorithm on real world datasets.

Connected through hyperedge HE-1 (induced by source paper "index based high dimensional cosine threshold querying with optimality guarantees"):
  with members:
    - "hd index pushing the scalability accuracy boundary for approximate knn search in high dimensional spaces"
    - "flash randomized algorithms accelerated over cpu gpu for ultra high dimensional similarity search"
    - "on the complexity of inner product similarity join"
    - "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)