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: "an iterative global structure assisted labeled network aligner"
  Abstract: Integrating data from heterogeneous sources is often modeled as merging graphs. Given two or more 'compatible', but not-isomorphic graphs, the first step is to identify a graph alignment, where a potentially partial mapping of vertices between two graphs is computed. A significant portion of the literature on this problem only takes the global structure of the input graphs into account. Only more recent ones additionally use vertex and edge attributes to achieve a more accurate alignment. However, these methods are not designed to scale to map large graphs arising in many modern applications. We propose a new iterative graph aligner, gsaNA, that uses the global structure of the graphs to significantly reduce the problem size and align large graphs with a minimal loss of information. Concretely, we show that our proposed technique is highly flexible, can be used to achieve higher recall, and it is orders of magnitudes faster than the current state of the art techniques.

Connected through hyperedge HE-1 (induced by source paper "elruna elimination rule based network alignment"):
  with members:
    - "regal representation learning based graph alignment"
    - "spectral alignment of graphs"
    - "advanced coarsening schemes for graph partitioning"
    - "relaxation based coarsening and multiscale graph organization"

Connected through hyperedge HE-2 (induced by source paper "unsupervised adversarial graph alignment with graph embedding"):
  with members:
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "adaptive sampling towards fast graph representation learning"
    - "billion scale similarity search with gpus"
    - "offline bilingual word vectors orthogonal transformations and the inverted softmax"
    - "node2vec scalable feature learning for networks"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"
    - "improving zero shot learning by mitigating the hubness problem"

Connected through hyperedge HE-3 (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"
    - "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"
    - "maximum quadratic assignment problem reduction from maximum label cover and lp based approximation algorithm"

Connected through hyperedge HE-4 (induced by source paper "efficient and high quality seeded graph matching employing high order structural information"):
  with members:
    - "motifs in temporal networks"
    - "seeded graph matching efficient algorithms and theoretical guarantees"
    - "block models and personalized pagerank"
    - "higher order organization of complex networks"
    - "pagerank beyond the web"
    - "studying user footprints in different online social networks"
    - "defining and evaluating network communities based on ground truth"
    - "de anonymizing social networks"

Connected through hyperedge HE-5 (induced by source paper "programming strategies for irregular algorithms on the emu chick"):
  with members:
    - "mathematical foundations of the graphblas"
    - "lagrangian relaxation applied to sparse global network alignment"

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.SI(Social and Information Networks)