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: "network inference via the time varying graphical lasso"
  Abstract: Many important problems can be modeled as a system of interconnected entities, where each entity is recording time-dependent observations or measurements. In order to spot trends, detect anomalies, and interpret the temporal dynamics of such data, it is essential to understand the relationships between the different entities and how these relationships evolve over time. In this paper, we introduce the time-varying graphical lasso (TVGL), a method of inferring time-varying networks from raw time series data. We cast the problem in terms of estimating a sparse time-varying inverse covariance matrix, which reveals a dynamic network of interdependencies between the entities. Since dynamic network inference is a computationally expensive task, we derive a scalable message-passing algorithm based on the Alternating Direction Method of Multipliers (ADMM) to solve this problem in an efficient way. We also discuss several extensions, including a streaming algorithm to update the model and incorporate new observations in real time. Finally, we evaluate our TVGL algorithm on both real and synthetic datasets, obtaining interpretable results and outperforming state-of-the-art baselines in terms of both accuracy and scalability.

Connected through hyperedge HE-1 (induced by source paper "cube2net efficient query specific network construction with data cube organization"):
  with members:
    - "similarity modeling on heterogeneous networks via automatic path discovery"
    - "neural embedding propagation on heterogeneous networks"
    - "relation learning on social networks with multi modal graph edge variational autoencoders"
    - "meta graph based hin spectral embedding methods analyses and insights"
    - "place deduplication with embeddings"
    - "query specific knowledge summarization with entity evolutionary networks"
    - "cubenet multi facet hierarchical heterogeneous network construction analysis and mining"
    - "user guided clustering in heterogeneous information networks via motif based comprehensive transcription"

Connected through hyperedge HE-2 (induced by source paper "learning graphs from data a signal representation perspective"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "graph learning from filtered signals graph system and diffusion kernel identification"
    - "graph learning under sparsity priors"
    - "blind identification of graph filters"
    - "graph based transform coding with application to image compression"
    - "signal processing on graphs causal modeling of un structured data"
    - "network topology inference from spectral templates"
    - "learning heat diffusion graphs"

Connected through hyperedge HE-3 (induced by source paper "design benchmarking and explainability analysis of a game theoretic framework towards energy efficiency in smart infrastructure"):
  with members:
    - "limiting gaming opportunities on incentive based demand response programs"
    - "a deep learning and gamification approach to energy conservation at nanyang technological university"
    - "a robust utility learning framework via inverse optimization"
    - "recurrent neural networks for driver activity anticipation via sensory fusion architecture"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "draw a recurrent neural network for image generation"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-4 (induced by source paper "a distributed method for fitting laplacian regularized stratified models"):
  with members:
    - "a survey on multi task learning"
    - "sparse matrix factorizations for fast linear solvers with application to laplacian systems"
    - "snap a general purpose network analysis and graph mining library"
    - "a unified alternating direction method of multipliers by majorization minimization"
    - "snapvx a network based convex optimization solver"
    - "network lasso clustering and optimization in large graphs"
    - "the numpy array a structure for efficient numerical computation"

Connected through hyperedge HE-5 (induced by source paper "a novel graphical lasso based approach towards segmentation analysis in energy game theoretic frameworks"):
  with members:
    - "delayed impact of fair machine learning"
    - "adaptive incentive design"
    - "a robust utility learning framework via inverse optimization"

Connected through hyperedge HE-6 (induced by source paper "efficient covariance estimation from temporal data"):
  with members:
    - "adam a method for stochastic optimization"
    - "discovering structure in high dimensional data through correlation explanation"
    - "inverse covariance estimation for high dimensional data in linear time and space spectral methods for riccati and sparse models"

Connected through hyperedge HE-7 (induced by source paper "query specific knowledge summarization with entity evolutionary networks"):
  with members:
    - "setexpan corpus based set expansion via context feature selection and rank ensemble"
    - "entity set search of scientific literature an unsupervised ranking approach"
    - "bringing structure into summaries crowdsourcing a benchmark corpus of concept maps"

Connected through hyperedge HE-8 (induced by source paper "fast estimation of causal interactions using wold processes"):
  with members:
    - "motifs in temporal networks"
    - "learning network of multivariate hawkes processes a time series approach"
    - "learning granger causality for hawkes processes"
    - "microscopic approach of a time elapsed neural model"
    - "a scalable asynchronous distributed algorithm for topic modeling"

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.LG(Machine Learning)