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 "multi resolution hashing for fast pairwise summations"):
  Abstract: A basic computational primitive in the analysis of massive datasets is summing simple functions over a large number of objects. Modern applications pose an additional challenge in that such functions often depend on a parameter vector $y$ (query) that is unknown a priori. Given a set of points $X\subset \mathbb{R}^{d}$ and a pairwise function $w:\mathbb{R}^{d}\times \mathbb{R}^{d}\to [0,1]$, we study the problem of designing a data-structure that enables sublinear-time approximation of the summation $Z_{w}(y)=\frac{1}{|X|}\sum_{x\in X}w(x,y)$ for any query $y\in \mathbb{R}^{d}$. By combining ideas from Harmonic Analysis (partitions of unity and approximation theory) with Hashing-Based-Estimators [Charikar, Siminelakis FOCS'17], we provide a general framework for designing such data structures through hashing that reaches far beyond what previous techniques allowed. #R##N#A key design principle is a collection of $T\geq 1$ hashing schemes with collision probabilities $p_{1},\ldots, p_{T}$ such that $\sup_{t\in [T]}\{p_{t}(x,y)\} = \Theta(\sqrt{w(x,y)})$. This leads to a data-structure that approximates $Z_{w}(y)$ using a sub-linear number of samples from each hash family. Using this new framework along with Distance Sensitive Hashing [Aumuller, Christiani, Pagh, Silvestri PODS'18], we show that such a collection can be constructed and evaluated efficiently for any log-convex function $w(x,y)=e^{\phi(\langle x,y\rangle)}$ of the inner product on the unit sphere $x,y\in \mathcal{S}^{d-1}$. #R##N#Our method leads to data structures with sub-linear query time that significantly improve upon random sampling and can be used for Kernel Density or Partition Function Estimation. We provide extensions of our result from the sphere to $\mathbb{R}^{d}$ and from scalar functions to vector functions.

Includes members:
  Member "hardness of approximate nearest neighbor search":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the i o complexity of the k nearest neighbor problem")
  Member "data dependent coresets for compressing neural networks with applications to generalization bounds":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "good subnetworks provably exist pruning via greedy forward selection")
  Member "hashing based estimators for kernel density in high dimensions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the gaussiansketch for almost relative error kernel distance")
  Member "on the fine grained complexity of empirical risk minimization kernel methods and neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "tight kernel query complexity of kernel ridge regression and kernel k means clustering")
  Member "stochastic optimization with bandit sampling":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "online newton step algorithm with estimated gradient")
  Member "fast amortized inference and learning in log linear models with randomly perturbed nearest neighbor search":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sampled softmax with random fourier features")
  Member "tight cell probe bounds for succinct boolean matrix vector multiplication":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "coarse grained complexity for dynamic algorithms")
  Member "distance sensitive hashing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "context aware sentence passage term importance estimation for first stage retrieval")

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)