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: "distributed methods for high dimensional and large scale tensor factorization"
  Abstract: Given a high-dimensional and large-scale tensor, how can we decompose it into latent factors? Can we process it on commodity computers with limited memory? These questions are closely related to recommendation systems exploiting context information such as time and location. They require tensor factorization methods scalable with both the dimension and size of a tensor. In this paper, we propose two distributed tensor factorization methods, SALS and CDTF. Both methods are scalable with all aspects of data, and they show an interesting trade-off between convergence speed and memory requirements. SALS updates a subset of the columns of a factor matrix at a time, and CDTF, a special case of SALS, updates one column at a time. On our experiment, only our methods factorize a 5-dimensional tensor with 1B observable entries, 10M mode length, and 1K rank, while all other state-of-the-art methods fail. Moreover, our methods require several orders of magnitude less memory than the competitors. We implement our methods on MapReduce with two widely applicable optimization techniques: local disk caching and greedy row assignment.

Connected through hyperedge HE-1 (induced by source paper "column wise element selection for computationally efficient nonnegative coupled matrix tensor factorization"):
  with members:
    - "extracting point of interest and classifying environment for low sampling crowd sensing smartphone sensor data"
    - "how to escape saddle points efficiently"

Connected through hyperedge HE-2 (induced by source paper "efficient nonnegative tensor factorization via saturating coordinate descent"):
  with members:
    - "non negative tensor factorization for human behavioral pattern mining in online games"

Connected through hyperedge HE-3 (induced by source paper "no place to hide catching fraudulent entities in tensors"):
  with members:
    - "spotting suspicious link behavior with fbox an adversarial perspective"

Connected through hyperedge HE-4 (induced by source paper "on optimizing distributed tucker decomposition for dense tensors"):
  with members:
    - "parallel tensor compression for large scale scientific data"
    - "decomposition of big tensors with low multilinear rank"

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)