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: "scalable realistic recommendation datasets through fractal expansions"
  Abstract: Recommender System research suffers currently from a disconnect between the size of academic data sets and the scale of industrial production systems. In order to bridge that gap we propose to generate more massive user/item interaction data sets by expanding pre-existing public data sets. User/item incidence matrices record interactions between users and items on a given platform as a large sparse matrix whose rows correspond to users and whose columns correspond to items. Our technique expands such matrices to larger numbers of rows (users), columns (items) and non zero values (interactions) while preserving key higher order statistical properties. We adapt the Kronecker Graph Theory to user/item incidence matrices and show that the corresponding fractal expansions preserve the fat-tailed distributions of user engagements, item popularity and singular value spectra of user/item interaction matrices. Preserving such properties is key to building large realistic synthetic data sets which in turn can be employed reliably to benchmark Recommender Systems and the systems employed to train them. We provide algorithms to produce such expansions and apply them to the MovieLens 20 million data set comprising 20 million ratings of 27K movies by 138K users. The resulting expanded data set has 10 billion ratings, 864K items and 2 million users in its smaller version and can be scaled up or down. A larger version features 655 billion ratings, 7 million items and 17 million users.

Connected through hyperedge HE-1 (induced by source paper "developing a recommendation benchmark for mlperf training and inference"):
  with members:
    - "deep learning recommendation model for personalization and recommendation systems"
    - "mlperf inference benchmark"
    - "deep cross network for ad click predictions"

Connected through hyperedge HE-2 (induced by source paper "mlperf training benchmark"):
  with members:
    - "deep learning recommendation model for personalization and recommendation systems"
    - "a modular benchmarking infrastructure for high performance and reproducible deep learning"
    - "optimizing network performance for distributed dnn training on gpu clusters imagenet alexnet training in 1 5 minutes"
    - "scalable methods for 8 bit training of neural networks"
    - "massively distributed sgd imagenet resnet 50 training in a flash"
    - "analysis of dawnbench a time to accuracy machine learning performance benchmark"
    - "nvidia tensor core programmability performance precision"
    - "image classification at supercomputer scale"

Connected through hyperedge HE-3 (induced by source paper "scaling up collaborative filtering data sets through randomized fractal expansions"):
  with members:
    - "towards neural mixture recommender for long range dependent user sequences"
    - "how algorithmic confounding in recommendation systems increases homogeneity and decreases utility"
    - "personalized top n sequential recommendation via convolutional sequence embedding"
    - "neural collaborative filtering"
    - "a dual framework for low rank tensor completion"
    - "structural deep embedding for hyper networks"
    - "efficient softmax approximation for gpus"
    - "a neural autoregressive approach to collaborative filtering"

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.IR(Information Retrieval)