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 "on the hardness of approximate and exact bichromatic maximum inner product"):
  Abstract: In this paper we study the (Bichromatic) Maximum Inner Product Problem (Max-IP), in which we are given sets $A$ and $B$ of vectors, and the goal is to find $a \in A$ and $b \in B$ maximizing inner product $a \cdot b$. Max-IP is very basic and serves as the base problem in the recent breakthrough of [Abboud et al., FOCS 2017] on hardness of approximation for polynomial-time problems. It is also used (implicitly) in the argument for hardness of exact $\ell_2$-Furthest Pair (and other important problems in computational geometry) in poly-log-log dimensions in [Williams, SODA 2018]. We have three main results regarding this problem. #R##N#First, we study the best multiplicative approximation ratio for Boolean Max-IP in sub-quadratic time. We show that, for Max-IP with two sets of $n$ vectors from $\{0,1\}^{d}$, there is an $n^{2 - \Omega(1)}$ time $\left( d/\log n \right)^{\Omega(1)}$-multiplicative-approximating algorithm, and we show this is conditionally optimal, as such a $\left(d/\log n\right)^{o(1)}$-approximating algorithm would refute SETH. #R##N#Second, we achieve a similar characterization for the best additive approximation error to Boolean Max-IP. We show that, for Max-IP with two sets of $n$ vectors from $\{0,1\}^{d}$, there is an $n^{2 - \Omega(1)}$ time $\Omega(d)$-additive-approximating algorithm, and this is conditionally optimal, as such an $o(d)$-approximating algorithm would refute SETH [Rubinstein, STOC 2018]. #R##N#Last, we revisit the hardness of solving Max-IP exactly for vectors with integer entries. We show that, under SETH, for Max-IP with sets of $n$ vectors from $\mathbb{Z}^{d}$ for some $d = 2^{O(\log^{*} n)}$, every exact algorithm requires $n^{2 - o(1)}$ time. With the reduction from [Williams, SODA 2018], it follows that $\ell_2$-Furthest Pair and Bichromatic $\ell_2$-Closest Pair in $2^{O(\log^{*} n)}$ dimensions require $n^{2 - o(1)}$ time.

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 "conditional lower bounds for all pairs max flow":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on two measures of distance between fully labelled trees")
  Member "conditional lower bounds for space time tradeoffs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "data structures meet cryptography 3sum with preprocessing")
  Member "distributed pcp theorems for hardness of approximation in p":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "graphs cannot be indexed in polynomial time for sub quadratic time string matching unless seth fails")
  Member "on the parameterized complexity of approximating dominating set":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fine grained complexity meets ip pspace")
  Member "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "new algorithms and hardness for incremental single source shortest paths in directed graphs")
  Member "on the complexity of closest pair via polar pair of point sets":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fine grained complexity meets ip pspace")
  Member "a framework for similarity search with space time tradeoffs using locality sensitive filtering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "lsf join locality sensitive filtering for distributed all pairs set similarity under skew")

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)