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 "inner product oracle can estimate and sample"):
  Abstract: Edge estimation problem in unweighted graphs using local and sometimes global queries is a fundamental problem in sublinear algorithms. It has been observed by Goldreich and Ron (Random Structures & Algorithms, 2008), that weighted edge estimation for weighted graphs require $\Omega(n)$ local queries, where $n$ denotes the number of vertices in the graph. To handle this problem, we introduce a new inner product query on matrices. Inner product query generalizes and unifies all previously used local queries on graphs used for estimating edges. With this new query, we show that weighted edge estimation in graphs with particular kind of weights can be solved using sublinear queries, in terms of the number of vertices. We also show that using this query we can solve the problem of the bilinear form estimation, and the problem of weighted sampling of entries of matrices induced by bilinear forms. This work is the first step towards weighted edge estimation mentioned in Goldreich and Ron (Random Structures & Algorithms, 2008).

Includes members:
  Member "sublinear time numerical linear algebra for structured matrices":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "robust and sample optimal algorithms for psd low rank approximation")
  Member "fine grained reductions from approximate counting to decision":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the fine grained complexity of parity problems")
  Member "testing matrix rank optimally":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "querying a matrix through matrix vector products")
  Member "edge estimation with independent set oracles":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximately counting and sampling small witnesses using a colourful decision oracle")
  Member "on sampling edges almost uniformly":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "computing and testing small connectivity in near linear time and queries via fast local cut algorithms")
  Member "computing exact minimum cuts without knowing the graph":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "two player hidden pointer chasing and multi pass lower bounds in turnstile streams")
  Member "approximately counting triangles in sublinear time":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "parallel algorithms for small subgraph counting")
  Member "the power of an example hidden set size approximation using group queries and conditional sampling":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "optimal randomized group testing algorithm to determine the number of defectives")

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.CC(Computational Complexity)