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: "maximum quadratic assignment problem reduction from maximum label cover and lp based approximation algorithm"
  Abstract: We show that for every positive $\epsilon > 0$, unless NP $\subset$ BPQP, it is impossible to approximate the maximum quadratic assignment problem within a factor better than $2^{\log^{1-\epsilon} n}$ by a reduction from the maximum label cover problem. Our result also implies that Approximate Graph Isomorphism is not robust and is in fact, $1 - \epsilon$ vs $\epsilon$ hard assuming the Unique Games Conjecture. #R##N#Then, we present an $O(\sqrt{n})$-approximation algorithm for the problem based on rounding of the linear programming relaxation often used in the state of the art exact algorithms.

Connected through hyperedge HE-1 (induced by source paper "from tree matching to sparse graph alignment"):
  with members:
    - "analysis of a canonical labeling algorithm for the alignment of correlated erd h o s r enyi graphs"
    - "spectral alignment of graphs"
    - "community detection thresholds and the weak ramanujan property"
    - "de anonymizing social networks"

Connected through hyperedge HE-2 (induced by source paper "spectre seedless network alignment via spectral centralities"):
  with members:
    - "regal representation learning based graph alignment"
    - "seeded graph matching via large neighborhood statistics"
    - "an iterative global structure assisted labeled network aligner"
    - "analysis of a canonical labeling algorithm for the alignment of correlated erd h o s r enyi graphs"
    - "gemsec graph embedding with self clustering"
    - "exact alignment recovery for correlated erdos renyi graphs"
    - "graph isomorphism in quasipolynomial time"
    - "spectral alignment of networks"

Connected through hyperedge HE-3 (induced by source paper "minimizing the union tight approximations for small set bipartite vertex expansion"):
  with members:
    - "approximation algorithms for hypergraph small set expansion and small set vertex expansion"
    - "everywhere sparse spanners via dense subgraphs"

Connected through hyperedge HE-4 (induced by source paper "spectral alignment of networks"):
  with members:
    - "triangular alignment tame a tensor based approach for higher order network alignment"
    - "lagrangian relaxation applied to sparse global network alignment"
    - "de anonymizing social networks"

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.CC(Computational Complexity)