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: "inferring graphs from cascades a sparse recovery framework"
  Abstract: In the Network Inference problem, one seeks to recover the edges of an unknown graph from the observations of cascades propagating over this graph. In this paper, we approach this problem from the sparse recovery perspective. We introduce a general model of cascades, including the voter model and the independent cascade model, for which we provide the first algorithm which recovers the graph's edges with high probability and $O(s\log m)$ measurements where $s$ is the maximum degree of the graph and $m$ is the number of nodes. Furthermore, we show that our algorithm also recovers the edge weights (the parameters of the diffusion process) and is robust in the context of approximate sparsity. Finally we prove an almost matching lower bound of $\Omega(s\log\frac{m}{s})$ and validate our approach empirically on synthetic graphs.

Connected through hyperedge HE-1 (induced by source paper "reconstruction of c c channel for p2p botnet"):
  with members:
    - "defense strategies against modern botnets"

Connected through hyperedge HE-2 (induced by source paper "learning graphs from linear measurements fundamental trade offs and applications"):
  with members:
    - "learning graphs from data a signal representation perspective"
    - "failure localization in power systems via tree partitions"
    - "learning identifiable gaussian bayesian networks in polynomial time and sample complexity"
    - "exact topology and parameter estimation in distribution grids with minimal observability"
    - "patopa a data driven parameter and topology joint estimation framework in distribution grids"
    - "learning linear structural equation models in polynomial time and sample complexity"
    - "information theoretic limits of bayesian network structure learning"
    - "learning sparse graphs under smoothness prior"

Connected through hyperedge HE-3 (induced by source paper "collaborative inference of coexisting information diffusions"):
  with members:
    - "diffusion and superposition distances for signals supported on networks"
    - "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 "reconstructing parameters of spreading models from partial observations"):
  with members:
    - "optimal deployment of resources for maximizing impact in spreading processes"
    - "estimating diffusion network structures recovery conditions sample complexity soft thresholding algorithm"
    - "reconstructing a graph from path traces"
    - "trace complexity of network inference"
    - "uncovering the temporal dynamics of diffusion networks"
    - "on the convexity of latent social network inference"

Connected through hyperedge HE-5 (induced by source paper "information theoretic lower bounds for recovery of diffusion network structures"):
  with members:
    - "estimating diffusion network structures recovery conditions sample complexity soft thresholding algorithm"
    - "trace complexity of network inference"
    - "uncovering the temporal dynamics of diffusion networks"
    - "on the convexity of latent social network inference"

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)