human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "practical data dependent metric compression with provable guarantees"):
  Abstract: We introduce a new distance-preserving compact representation of multi-dimensional point-sets. Given $n$ points in a $d$-dimensional space where each coordinate is represented using $B$ bits (i.e., $dB$ bits per point), it produces a representation of size $O( d \log(d B/\epsilon) + \log n)$ bits per point from which one can approximate the distances up to a factor of $1 \pm \epsilon$. Our algorithm almost matches the recent bound of~\cite{indyk2017near} while being much simpler. We compare our algorithm to Product Quantization (PQ)~\cite{jegou2011product}, a state of the art heuristic metric compression method. We evaluate both algorithms on several data sets: SIFT (used in \cite{jegou2011product}), MNIST~\cite{lecun1998mnist}, New York City taxi time series~\cite{guha2016robust} and a synthetic one-dimensional data set embedded in a high-dimensional space. With appropriately tuned parameters, our algorithm produces representations that are comparable to or better than those produced by PQ, while having provable guarantees on its performance.

Includes members:
  Member "near optimal euclidean metric compression":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the space complexity of inner product filters")
  Member "learning to hash for indexing big data a survey":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "random vlad based deep hashing for efficient image retrieval")
  Member "feature hashing for large scale multitask learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hallucinating statistical moment and subspace descriptors from object and saliency detectors for action recognition")

Question: Please tell me which class the source paper 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)