human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "orthogonal vectors indexing"
  Abstract: In the recent years, intensive research work has been dedicated to prove conditional lower bounds in order to reveal the inner structure of the class P. These conditional lower bounds are based on many popular conjectures on well-studied problems. One of the most heavily used conjectures is the celebrated Strong Exponential Time Hypothesis (SETH). It turns out that conditional hardness proved based on SETH goes, in many cases, through an intermediate problem - the Orthogonal Vectors (OV) problem. #R##N#Almost all research work regarding conditional lower bound was concentrated on time complexity. Very little attention was directed toward space complexity. In a recent work, Goldstein et al.[WADS 2017] set the stage for proving conditional lower bounds regarding space and its interplay with time. In this spirit, it is tempting to investigate the space complexity of a data structure variant of OV which is called \emph{OV indexing}. In this problem $n$ boolean vectors of size $c\log{n}$ are given for preprocessing. As a query, a vector $v$ is given and we are required to verify if there is an input vector that is orthogonal to it or not. #R##N#This OV indexing problem is interesting in its own, but it also likely to have strong implications on problems known to be conditionally hard, in terms of time complexity, based on OV. Having this in mind, we study OV indexing in this paper from many aspects. We give some space-efficient algorithms for the problem, show a tradeoff between space and query time, describe how to solve its reporting variant, shed light on an interesting connection between this problem and the well-studied SetDisjointness problem and demonstrate how it can be solved more efficiently on random input.

Connected through hyperedge HE-1 (induced by source paper "data structures meet cryptography 3sum with preprocessing"):
  with members:
    - "the strong 3sum indexing conjecture is false"
    - "equivalence of systematic linear data structures and matrix rigidity"
    - "lower bounds for oblivious data structures"
    - "static data structure lower bounds imply rigidity"
    - "how hard is it to find honest witnesses"
    - "conditional lower bounds for space time tradeoffs"
    - "clustered integer 3sum via additive combinatorics"
    - "popular conjectures imply strong lower bounds for dynamic problems"

Connected through hyperedge HE-2 (induced by source paper "3sum with preprocessing algorithms lower bounds and cryptographic applications"):
  with members:
    - "encoding 3sum"
    - "static data structure lower bounds imply rigidity"
    - "near optimal linear decision trees for k sum and related problems"
    - "conditional lower bounds for space time tradeoffs"
    - "clustered integer 3sum via additive combinatorics"
    - "threesomes degenerates and love triangles"
    - "on hardness of jumbled indexing"
    - "higher lower bounds from the 3sum conjecture"

Question: Please tell me which class the center node 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)