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 "the space complexity of inner product filters"):
  Abstract: Motivated by the problem of filtering candidate pairs in inner product similarity joins we study the following inner product estimation problem: Given parameters $d\in {\bf N}$, $\alpha>\beta\geq 0$ and unit vectors $x,y\in {\bf R}^{d}$ consider the task of distinguishing between the cases $\langle x, y\rangle\leq\beta$ and $\langle x, y\rangle\geq \alpha$ where $\langle x, y\rangle = \sum_{i=1}^d x_i y_i$ is the inner product of vectors $x$ and $y$. The goal is to distinguish these cases based on information on each vector encoded independently in a bit string of the shortest length possible. In contrast to much work on compressing vectors using randomized dimensionality reduction, we seek to solve the problem deterministically, with no probability of error. Inner product estimation can be solved in general via estimating $\langle x, y\rangle$ with an additive error bounded by $\varepsilon = \alpha - \beta$. We show that $d \log_2 \left(\tfrac{\sqrt{1-\beta}}{\varepsilon}\right) \pm \Theta(d)$ bits of information about each vector is necessary and sufficient. Our upper bound is constructive and improves a known upper bound of $d \log_2(1/\varepsilon) + O(d)$ by up to a factor of 2 when $\beta$ is close to $1$. The lower bound holds even in a stronger model where one of the vectors is known exactly, and an arbitrary estimation function is allowed.

Includes members:
  Member "norm ranging lsh for maximum inner product search":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "subsets and supermajorities unifying hashing based set similarity search")
  Member "approximate nearest neighbors in limited space":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "race sub linear memory sketches for approximate near neighbor search on streaming data")
  Member "adaptive mapreduce similarity joins":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lsf join locality sensitive filtering for distributed all pairs set similarity under skew")
  Member "practical data dependent metric compression with provable guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scalable nearest neighbor search for optimal transport")
  Member "distributed pcp theorems for hardness of approximation in p":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "graphs cannot be indexed in polynomial time for sub quadratic time string matching unless seth fails")
  Member "flexpoint an adaptive numerical format for efficient training of deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "shifted and squeezed 8 bit floating point format for low precision training of deep neural networks")
  Member "bolt accelerated data mining with fast vector compression":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "quicker adc unlocking the hidden potential of product quantization with simd")
  Member "scalable and robust set similarity join":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "blocking and filtering techniques for entity resolution a survey")

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)