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: "parallel algorithms for constrained tensor factorization via alternating direction method of multipliers"
  Abstract: Tensor factorization has proven useful in a wide range of applications, from sensor array processing to communications, speech and audio signal processing, and machine learning. With few recent exceptions, all tensor factorization algorithms were originally developed for centralized, in-memory computation on a single machine; and the few that break away from this mold do not easily incorporate practically important constraints, such as non-negativity. A new constrained tensor factorization framework is proposed in this paper, building upon the Alternating Direction Method of Multipliers (ADMoM). It is shown that this simplifies computations, bypassing the need to solve constrained optimization problems in each iteration; and it naturally leads to distributed algorithms suitable for parallel implementation. This opens the door for many emerging big data-enabled applications. The methodology is exemplified using non-negativity as a baseline constraint, but the proposed framework can incorporate many other types of constraints. Numerical experiments are encouraging, indicating that ADMoM-based non-negative tensor factorization (NTF) has high potential as an alternative to state-of-the-art approaches.

Connected through hyperedge HE-1 (induced by source paper "accelerating admm for efficient simulation and optimization"):
  with members:
    - "parallel and scalable heat methods for geodesic distance computation"
    - "anderson acceleration for geometry optimization and physics simulation"
    - "plug and play admm for image restoration fixed point convergence and applications"
    - "distributed maximum likelihood sensor network localization"

Connected through hyperedge HE-2 (induced by source paper "joint embedding of meta path and meta graph for heterogeneous information networks"):
  with members:
    - "multi round influence maximization"
    - "sequential keystroke behavioral biometrics for mobile user identification via multi view deep learning"
    - "contaminant removal for android malware detection systems"
    - "online unsupervised multi view feature selection"
    - "node2vec scalable feature learning for networks"
    - "task guided and path augmented heterogeneous network embedding for author identification"
    - "line large scale information network embedding"
    - "deepwalk online learning of social representations"

Connected through hyperedge HE-3 (induced by source paper "application of preconditioned alternating direction method of multipliers in depth from focal stack"):
  with members:
    - "variational depth from focus reconstruction"
    - "a field guide to forward backward splitting with a fasta implementation"
    - "playing with duality an overview of recent primal dual approaches for solving large scale optimization problems"
    - "an alternating direction algorithm for matrix completion with nonnegative factors"

Connected through hyperedge HE-4 (induced by source paper "multiscale analysis for higher order tensors"):
  with members:
    - "tensor decompositions for signal processing applications from two way to multiway component analysis"
    - "generalized fisher score for feature selection"

Connected through hyperedge HE-5 (induced by source paper "tensor networks for dimensionality reduction and large scale optimization part 1 low rank tensor decompositions"):
  with members:
    - "randomized single view algorithms for low rank matrix approximation"
    - "group component analysis for multiblock data common and individual feature extraction"
    - "convolutional rectifier networks as generalized tensor decompositions"
    - "tensor networks for latent variable analysis part i algorithms for tensor train decomposition"
    - "linked component analysis from matrices to high order tensors applications to biomedical data"
    - "parallel tensor compression for large scale scientific data"
    - "tensor ring decomposition"
    - "smooth parafac decomposition for tensor completion"

Connected through hyperedge HE-6 (induced by source paper "stochastic variance reduced admm"):
  with members:
    - "scalable stochastic alternating direction method of multipliers"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "fast stochastic alternating direction method of multipliers"
    - "online alternating direction method"

Connected through hyperedge HE-7 (induced by source paper "convex optimization for non convex problems via column generation"):
  with members:
    - "tracking objects with higher order interactions using delayed column generation"
    - "tensor decompositions for learning latent variable models"
    - "tightening lp relaxations for map using message passing"
    - "a method of moments for mixture models and hidden markov models"
    - "most tensor problems are np hard"

Connected through hyperedge HE-8 (induced by source paper "non negative factorization of the occurrence tensor from financial contracts"):
  with members:
    - "adaptive admm with spectral penalty parameter selection"
    - "exploiting lists of names for named entity identification of financial institutions from unstructured documents"
    - "tensor decompositions for learning latent variable models"

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.NA(Numerical Analysis)