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: "alignment of dynamic networks"
  Abstract: Networks can model real-world systems in a variety of domains. Network alignment (NA) aims to find a node mapping that conserves similar regions between compared networks. NA is applicable to many fields, including computational biology, where NA can guide the transfer of biological knowledge from well- to poorly-studied species across aligned network regions. Existing NA methods can only align static networks. However, most complex real-world systems evolve over time and should thus be modeled as dynamic networks. We hypothesize that aligning dynamic network representations of evolving systems will produce superior alignments compared to aligning the systems' static network representations, as is currently done. For this purpose, we introduce the first ever dynamic NA method, DynaMAGNA++. This proof-of-concept dynamic NA method is an extension of a state-of-the-art static NA method, MAGNA++. Even though both MAGNA++ and DynaMAGNA++ optimize edge as well as node conservation across the aligned networks, MAGNA++ conserves static edges and similarity between static node neighborhoods, while DynaMAGNA++ conserves dynamic edges (events) and similarity between evolving node neighborhoods. For this purpose, we introduce the first ever measure of dynamic edge conservation and rely on our recent measure of dynamic node conservation. Importantly, the two dynamic conservation measures can be optimized using any state-of-the-art NA method and not just MAGNA++. We confirm our hypothesis that dynamic NA is superior to static NA, under fair comparison conditions, on synthetic and real-world networks, in computational biology and social network domains. DynaMAGNA++ is parallelized and it includes a user-friendly graphical interface.

Connected through hyperedge HE-1 (induced by source paper "dna dynamic social network alignment"):
  with members:
    - "unsupervised user identity linkage via factoid embedding"
    - "meta diagram based active social networks alignment"
    - "adversarially regularized graph autoencoder for graph embedding"
    - "network representation learning a survey"
    - "timers error bounded svd restart on dynamic networks"
    - "a survey on network embedding"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "comparing temporal graphs using dynamic time warping"):
  with members:
    - "dynamic time warping in strongly subquadratic time algorithms for the low distance regime and approximate evaluation"
    - "temporal matching"
    - "multistage problems on a global budget"
    - "computing maximum matchings in temporal graphs"
    - "the computational complexity of finding temporal paths under waiting time constraints"
    - "fast exact dynamic time warping on run length encoded time series"
    - "multi net a scalable multiplex network embedding framework"
    - "the complexity of finding small separators in temporal graphs"

Connected through hyperedge HE-3 (induced by source paper "graphlets versus node2vec and struc2vec in the task of network alignment"):
  with members:
    - "struc2vec learning node representations from structural identity"
    - "predicting multicellular function through multi layer tissue networks"
    - "a survey on network embedding"
    - "node2vec scalable feature learning for networks"
    - "a fast sampling method of exploring graphlet degrees of large directed and undirected graphs"
    - "evaluating link prediction methods"
    - "exploring the structure and function of temporal networks with dynamic graphlets"
    - "clustering and community detection in directed networks a survey"

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)