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: "similarity preserving compressions of high dimensional sparse data"
  Abstract: The rise of internet has resulted in an explosion of data consisting of millions of articles, images, songs, and videos. Most of this data is high dimensional and sparse. The need to perform an efficient search for similar objects in such high dimensional big datasets is becoming increasingly common. Even with the rapid growth in computing power, the brute-force search for such a task is impractical and at times impossible. Therefore it is quite natural to investigate the techniques that compress the dimension of the data-set while preserving the similarity between data objects. #R##N#In this work, we propose an efficient compression scheme mapping binary vectors into binary vectors and simultaneously preserving Hamming distance and Inner Product. The length of our compression depends only on the sparsity and is independent of the dimension of the data. Moreover our schemes provide one-shot solution for Hamming distance and Inner Product, and work in the streaming setting as well. In contrast with the "local projection" strategies used by most of the previous schemes, our scheme combines (using sparsity) the following two strategies: $1.$ Partitioning the dimensions into several buckets, $2.$ Then obtaining "global linear summaries" in each of these buckets. We generalize our scheme for real-valued data and obtain compressions for Euclidean distance, Inner Product, and $k$-way Inner Product.

Connected through hyperedge HE-1 (induced by source paper "fixed length bit string representation of fingerprint by normalized local structures"):
  with members:
    - "ranking based locality sensitive hashing enabled cancelable biometrics index of max hashing"
    - "efficient compression technique for sparse sets"

Connected through hyperedge HE-2 (induced by source paper "efficient compression technique for sparse sets"):
  with members:
    - "approximating higher order distances using random projections"

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)