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: "diffusion and superposition distances for signals supported on networks"
  Abstract: We introduce the diffusion and superposition distances as two metrics to compare signals supported in the nodes of a network. Both metrics consider the given vectors as initial temperature distributions and diffuse heat trough the edges of the graph. The similarity between the given vectors is determined by the similarity of the respective diffusion profiles. The superposition distance computes the instantaneous difference between the diffused signals and integrates the difference over time. The diffusion distance determines a distance between the integrals of the diffused signals. We prove that both distances define valid metrics and that they are stable to perturbations in the underlying network. We utilize numerical experiments to illustrate their utility in classifying signals in a synthetic network as well as in classifying ovarian cancer histologies using gene mutation profiles of different patients. We also reinterpret diffusion as a transformation of interrelated feature spaces and use it as preprocessing tool for learning. We use diffusion to increase the accuracy of handwritten digit classification.

Connected through hyperedge HE-1 (induced by source paper "deep geometric knowledge distillation with graphs"):
  with members:
    - "and the bit goes down revisiting the quantization of neural networks"
    - "triplet distillation for deep face recognition"
    - "relational knowledge distillation"
    - "attention based pruning for shift networks"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "learning to prune filters in convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "laplacian networks bounding indicator function smoothness for neural networks robustness"

Connected through hyperedge HE-2 (induced by source paper "a distance measure for the analysis of polar opinion dynamics in social networks"):
  with members:
    - "a tutorial on modeling and analysis of dynamic social networks part i"
    - "learning and forecasting opinion dynamics in social networks"
    - "the bursty dynamics of the twitter information network"
    - "graph based anomaly detection and description a survey"
    - "deltacon a principled massive graph similarity function"
    - "netsimile a scalable approach to size independent network similarity"
    - "user level sentiment analysis incorporating social networks"
    - "uncovering social network sybils in the wild"

Connected through hyperedge HE-3 (induced by source paper "collaborative inference of coexisting information diffusions"):
  with members:
    - "inferring graphs from cascades a sparse recovery framework"
    - "predicting social links for new users across aligned heterogeneous social networks"
    - "inferring networks of diffusion and influence"
    - "on the convexity of latent social network inference"

Connected through hyperedge HE-4 (induced by source paper "graph laplacian regularization for image denoising analysis in the continuous domain"):
  with members:
    - "random walk graph laplacian based smoothness prior for soft decoding of jpeg images"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "discrete signal processing on graphs"

Connected through hyperedge HE-5 (induced by source paper "graph laplacian regularization for inverse imaging analysis in the continuous domain"):
  with members:
    - "random walk graph laplacian based smoothness prior for soft decoding of jpeg images"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "discrete signal processing on graphs"

Connected through hyperedge HE-6 (induced by source paper "identifying influential links for event propagation on twitter a network of networks approach"):
  with members:
    - "spectral projector based graph fourier transforms"
    - "principal patterns on graphs discovering coherent structures in datasets"
    - "information diffusion and external influence in networks"

Connected through hyperedge HE-7 (induced by source paper "toward an uncertainty principle for weighted graphs"):
  with members:
    - "a spectral graph uncertainty principle"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "perfect reconstruction two channel wavelet filter banks for graph structured data"

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)