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: "estimating diffusion network structures recovery conditions sample complexity soft thresholding algorithm"
  Abstract: Information spreads across social and technological networks, but often the network structures are hidden from us and we only observe the traces left by the diffusion processes, called cascades. Can we recover the hidden network structures from these observed cascades? What kind of cascades and how many cascades do we need? Are there some network structures which are more difficult than others to recover? Can we design efficient inference algorithms with provable guarantees? #R##N#Despite the increasing availability of cascade data and methods for inferring networks from these data, a thorough theoretical understanding of the above questions remains largely unexplored in the literature. In this paper, we investigate the network structure inference problem for a general family of continuous-time diffusion models using an $l_1$-regularized likelihood maximization framework. We show that, as long as the cascade sampling process satisfies a natural incoherence condition, our framework can recover the correct network structure with high probability if we observe $O(d^3 \log N)$ cascades, where $d$ is the maximum number of parents of a node and $N$ is the total number of nodes. Moreover, we develop a simple and efficient soft-thresholding inference algorithm, which we use to illustrate the consequences of our theoretical results, and show that our framework outperforms other alternatives in practice.

Connected through hyperedge HE-1 (induced by source paper "learning mixtures of graphs from epidemic cascades"):
  with members:
    - "learning graphs from noisy epidemic cascades"
    - "meta learning with relational information for short sequences"
    - "em converges for a mixture of many linear regressions"
    - "the cost of uncertainty in curing epidemics"
    - "fake news mitigation via point process based intervention"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"
    - "characterization and inference of graph diffusion processes from observations of stationary signals"
    - "solving a mixture of many random linear equations by tensor decomposition and alternating minimization"

Connected through hyperedge HE-2 (induced by source paper "when less is more systematic analysis of cascade based community detection"):
  with members:
    - "systematic analysis of cluster similarity indices towards bias free cluster validation"
    - "community detection through likelihood optimization in search of a sound model"
    - "dani a fast diffusion aware network inference algorithm"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "metrics for community analysis a survey"
    - "modeling information propagation with survival theory"
    - "structure and dynamics of information pathways in online media"
    - "submodular inference of diffusion networks from multiple trees"

Connected through hyperedge HE-3 (induced by source paper "provable computational and statistical guarantees for efficient learning of continuous action graphical games"):
  with members:
    - "learning quadratic games on networks"
    - "learning graphical games from behavioral data sufficient and necessary conditions"
    - "targeting interventions in networks"
    - "from behavior to sparse graphical games efficient recovery of equilibria"
    - "on influence stable behavior and the most influential individuals in networks a game theoretic approach"
    - "graphical models for game theory"
    - "learning the structure and parameters of large population graphical games from behavioral data"
    - "on the complexity of nash equilibria of action graph games"

Connected through hyperedge HE-4 (induced by source paper "reconstruction of c c channel for structured p2p botnet"):
  with members:
    - "topology discovery of sparse random graphs with few participants"
    - "defense strategies against modern botnets"

Connected through hyperedge HE-5 (induced by source paper "contrastive training for models of information cascades"):
  with members:
    - "modeling information propagation with survival theory"
    - "trace complexity of network inference"
    - "structure and dynamics of information pathways in online media"
    - "uncovering the temporal dynamics of diffusion networks"
    - "on the convexity of latent social network inference"

Connected through hyperedge HE-6 (induced by source paper "distilling information reliability and source trustworthiness from digital traces"):
  with members:
    - "uncovering the dynamics of crowdlearning and the value of knowledge"
    - "multistage campaigning in social networks"
    - "assessing human error against a benchmark of perfection"
    - "smart broadcasting do you want to be seen"
    - "from data fusion to knowledge fusion"
    - "coevolve a joint point process model for information diffusion and network co evolution"
    - "learning and forecasting opinion dynamics in social networks"
    - "knowledge based trust estimating the trustworthiness of web sources"

Connected through hyperedge HE-7 (induced by source paper "robust influence maximization"):
  with members:
    - "robust influence maximization"
    - "online influence maximization"
    - "stability of influence maximization"
    - "combinatorial multi armed bandit and its extension to probabilistically triggered arms"
    - "trace complexity of network inference"
    - "analytical approach to parallel repetition"
    - "scalable influence estimation in continuous time diffusion networks"
    - "maximizing social influence in nearly optimal time"

Connected through hyperedge HE-8 (induced by source paper "submodular variational inference for network reconstruction"):
  with members:
    - "polyhedral aspects of submodularity convexity and concavity"
    - "seeing the unseen network inferring hidden social ties from respondent driven sampling"
    - "fast semidifferential based submodular function optimization"
    - "submodular inference of diffusion networks from multiple trees"
    - "uncovering the temporal dynamics of diffusion networks"
    - "topology discovery of sparse random graphs with few participants"

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)