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: "streaming recommender systems"
  Abstract: The increasing popularity of real-world recommender systems produces data continuously and rapidly, and it becomes more realistic to study recommender systems under streaming scenarios. Data streams present distinct properties such as temporally ordered, continuous and high-velocity, which poses tremendous challenges to traditional recommender systems. In this paper, we investigate the problem of recommendation with stream inputs. In particular, we provide a principled framework termed sRec, which provides explicit continuous-time random process models of the creation of users and topics, and of the evolution of their interests. A variational Bayesian approach called recursive meanfield approximation is proposed, which permits computationally efficient instantaneous on-line inference. Experimental results on several real-world datasets demonstrate the advantages of our sRec over other state-of-the-arts.

Connected through hyperedge HE-1 (induced by source paper "autoemb automated embedding dimensionality search in streaming recommendations"):
  with members:
    - "adabert task adaptive bert compression with differentiable neural architecture search"
    - "mixed dimension embeddings with application to memory efficient recommendation systems"
    - "neural input search for large scale recommendation models"
    - "deep learning based recommender system a survey and new perspectives"
    - "regularized evolution for image classifier architecture search"
    - "neural architecture optimization"
    - "path level network transformation for efficient architecture search"
    - "darts differentiable architecture search"

Connected through hyperedge HE-2 (induced by source paper "reinforcement learning to optimize long term user engagement in recommender systems"):
  with members:
    - "recommendations with negative feedback via pairwise deep reinforcement learning"
    - "deep reinforcement learning for page wise recommendations"
    - "reinforcement learning for online information seeking"
    - "more robust doubly robust off policy evaluation"
    - "deep reinforcement learning for list wise recommendations"
    - "neural collaborative filtering"
    - "neural attentive session based recommendation"
    - "a neural autoregressive approach to collaborative filtering"

Connected through hyperedge HE-3 (induced by source paper "efficient representation learning using random walks for dynamic graphs"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "network embedding as matrix factorization unifying deepwalk line pte and node2vec"
    - "verse versatile graph embeddings from similarity measures"
    - "network representation learning a survey"
    - "representation learning on graphs methods and applications"
    - "the spacey random walk a stochastic process for higher order data"
    - "attributed network embedding for learning in a dynamic environment"
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-4 (induced by source paper "coupled variational recurrent collaborative filtering"):
  with members:
    - "deep learning based recommender system a survey and new perspectives"
    - "sequential variational autoencoders for collaborative filtering"
    - "a hybrid variational autoencoder for collaborative filtering"
    - "recurrent poisson factorization for temporal recommendation"
    - "neural collaborative filtering"
    - "discrete event continuous time rnns"
    - "collaborative recurrent autoencoder recommend while learning to fill in the blanks"
    - "session based recommendations with recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "online social media recommendation over streams"):
  with members:
    - "fast matrix factorization for online recommendation with implicit feedback"

Connected through hyperedge HE-6 (induced by source paper "streaming graph neural networks"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "dyngem deep embedding method for dynamic graphs"
    - "graph networks as learnable physics engines for inference and control"
    - "relational inductive biases deep learning and graph networks"
    - "timers error bounded svd restart on dynamic networks"
    - "know evolve deep temporal reasoning for dynamic knowledge graphs"
    - "neural message passing for quantum chemistry"
    - "geometric deep learning going beyond euclidean data"

Connected through hyperedge HE-7 (induced by source paper "dynamic online gradient descent with improved query complexity a theoretical revisit"):
  with members:
    - "introduction to online convex optimization"
    - "fast rates for online gradient descent without strong convexity via hoffman s bound"
    - "convergence of unregularized online learning algorithms"
    - "a modular analysis of adaptive non convex optimization optimism composite objectives and variational bounds"
    - "online optimization in dynamic environments improved regret rates for strongly convex problems"
    - "tracking slowly moving clairvoyant optimal dynamic regret of online learning with true and noisy gradient"
    - "online optimization competing with dynamic comparators"
    - "online gradient descent in function space"

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)