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: "snapvx a network based convex optimization solver"
  Abstract: SnapVX is a high-performance Python solver for convex optimization problems defined on networks. For these problems, it provides a fast and scalable solution with guaranteed global convergence. SnapVX combines the capabilities of two open source software packages: Snap.py and CVXPY. Snap.py is a large scale graph processing library, and CVXPY provides a general modeling framework for small-scale subproblems. SnapVX offers a customizable yet easy-to-use interface with out-of-the-box functionality. Based on the Alternating Direction Method of Multipliers (ADMM), it is able to efficiently store, analyze, and solve large optimization problems from a variety of different applications. Documentation, examples, and more can be found on the SnapVX website at this http URL

Connected through hyperedge HE-1 (induced by source paper "fast local linear regression with anchor regularization"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "optuna a next generation hyperparameter optimization framework"
    - "location centered house price prediction a multi task learning approach"
    - "network lasso clustering and optimization in large graphs"

Connected through hyperedge HE-2 (induced by source paper "a survey of optimization methods from a machine learning perspective"):
  with members:
    - "stochastic conjugate gradient algorithm with variance reduction"
    - "compatible natural gradient policy search"
    - "on the convergence of adam and beyond"
    - "normalized direction preserving adam"
    - "recent advances in recurrent neural networks"
    - "task agnostic meta learning for few shot learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-3 (induced by source paper "a distributed method for fitting laplacian regularized stratified models"):
  with members:
    - "network inference via the time varying graphical lasso"
    - "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"
    - "network lasso clustering and optimization in large graphs"
    - "the numpy array a structure for efficient numerical computation"

Connected through hyperedge HE-4 (induced by source paper "network inference via the time varying graphical lasso"):
  with members:
    - "network lasso clustering and optimization in large graphs"
    - "fused multiple graphical lasso"
    - "inferring networks of diffusion and influence"
    - "sparse inverse covariance selection via alternating linearization methods"
    - "on the convexity of latent social network inference"

Connected through hyperedge HE-5 (induced by source paper "testing fine grained parallelism for the admm on a factor graph"):
  with members:
    - "an improved three weight message passing algorithm"
    - "asynchronous distributed optimization using a randomized alternating direction method of multipliers"
    - "high speed compressed sensing reconstruction in dynamic parallel mri using augmented lagrangian and parallel processing"

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)