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 networks"
  Abstract: Network alignment refers to the problem of nding a bijective mapping across vertices of two graphs to maximize the number of overlapping edges and/or to minimize the number of mismatched interactions across networks. This problem arises in many elds such as computational biology, social sciences and computer vision and is often cast as an expensive quadratic assignment problem (QAP). Although spectral methods have received signicant attention in dierent network science problems such as network clustering, the use of spectral techniques in the network alignment problem has been limited partially owing to the lack of principled connections between spectral methods and relaxations of the network alignment optimization. In this paper, we propose a network alignment framework that uses an orthogonal relaxation of the underlying QAP in a maximum weight bipartite matching optimization. Our method takes into account the ellipsoidal level sets of the quadratic objective function by exploiting eigenvalues and eigenvectors of (transformations of) adjacency graphs. Our framework not only can be employed to provide a theoretical justication for existing heuristic spectral network alignment methods, but it also leads to a new scalable network alignment algorithm which outperforms existing ones over various synthetic and real networks. Moreover, we generalize the objective function of the network alignment problem to consider both matched and mismatched interactions in a standard QAP formulation. This can be critical in applications where networks have low similarity and therefore we expect more mismatches than matches. We assess the eectiveness of our proposed method theoretically for certain classes of networks, through simulations over various synthetic network models, and in two real-data applications; in comparative analysis of gene regulatory networks across human, y and worm, and in user de-anonymization over twitter follower subgraphs.

Connected through hyperedge HE-1 (induced by source paper "deep adversarial network alignment"):
  with members:
    - "spectre seedless network alignment via spectral centralities"
    - "struc2vec learning node representations from structural identity"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "irgan a minimax game for unifying generative and discriminative information retrieval models"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "node2vec scalable feature learning for networks"
    - "image to image translation with conditional adversarial networks"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "rigid graph alignment"):
  with members:
    - "low rank methods for multiple network alignment"
    - "triangular alignment tame a tensor based approach for higher order network alignment"
    - "rdf graph alignment with bisimulation"

Connected through hyperedge HE-3 (induced by source paper "solving partial assignment problems using random clique complexes"):
  with members:
    - "exponential family graph matching and ranking"
    - "learning graph matching"

Connected through hyperedge HE-4 (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"
    - "maximum quadratic assignment problem reduction from maximum label cover and lp based approximation algorithm"

Connected through hyperedge HE-5 (induced by source paper "partial recovery of erd h o s r e nyi graph alignment via k core alignment"):
  with members:
    - "fundamental limits of database alignment"
    - "nearly efficient algorithms for the graph matching problem on correlated random graphs"
    - "seeded graph matching via large neighborhood statistics"
    - "typicality matching for pairs of correlated graphs"
    - "blind de anonymization attacks using social networks"
    - "the computer science and physics of community detection landscapes phase transitions and hardness"
    - "exact alignment recovery for correlated erdos renyi graphs"

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)