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: "spectral alignment of graphs"
  Abstract: Graph alignment refers to the problem of finding a bijective mapping across vertices of two graphs such that, if two nodes are connected in the first graph, their images are connected in the second graph. This problem arises in many fields such as computational biology, social sciences, and computer vision and is often cast as a quadratic assignment problem (QAP). Most standard graph alignment methods consider an optimization that maximizes the number of matches between the two graphs, ignoring the effect of mismatches. We propose a generalized graph alignment formulation that considers both matches and mismatches in a standard QAP formulation. This modification can have a major impact in aligning graphs with different sizes and heterogenous edge densities. Moreover, we propose two methods for solving the generalized graph alignment problem based on spectral decomposition of matrices. We compare the performance of proposed methods with some existing graph alignment algorithms including Natalie2, GHOST, IsoRank, NetAlign, Klau's approach as well as a semidefinite programming-based method over various synthetic and real graph models. Our proposed method based on simultaneous alignment of multiple eigenvectors leads to consistently good performance in different graph models. In particular, in the alignment of regular graph structures which is one of the most difficult graph alignment cases, our proposed method significantly outperforms other methods.

Connected through hyperedge HE-1 (induced by source paper "elruna elimination rule based network alignment"):
  with members:
    - "regal representation learning based graph alignment"
    - "an iterative global structure assisted labeled network aligner"
    - "advanced coarsening schemes for graph partitioning"
    - "relaxation based coarsening and multiscale graph organization"

Connected through hyperedge HE-2 (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"
    - "maximum quadratic assignment problem reduction from maximum label cover and lp based approximation algorithm"
    - "community detection thresholds and the weak ramanujan property"
    - "de anonymizing social networks"

Connected through hyperedge HE-3 (induced by source paper "analysis of a canonical labeling algorithm for the alignment of correlated erd h o s r enyi graphs"):
  with members:
    - "nearly efficient algorithms for the graph matching problem on correlated random graphs"
    - "seeded graph matching via large neighborhood statistics"
    - "reconciling graphs and sets of sets"
    - "an information theoretic framework for active de anonymization in social networks based on group memberships"
    - "exact alignment recovery for correlated erdos renyi graphs"
    - "optimal de anonymization in random graphs with community structure"
    - "an efficient reconciliation algorithm for 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.DS(Data Structures and Algorithms)