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: "learning based dynamic cache management in a cloud"
  Abstract: Caches are an important component of modern computing systems given their significant impact on performance. In particular, caches play a key role in the cloud due to the nature of large-scale, data-intensive processing. One of the key challenges for the cloud providers is how to share the caching capacity among tenants, under the circumstance that each often requires a different degree of quality of service (QoS) with respect to data access performance. The invariant is that the individual tenants' QoS requirements should be satisfied while the cache usage is optimized in a system-wide manner. In this paper, we introduce a learning-based approach for dynamic cache management in a cloud, which is based on the estimation of data access pattern of a tenant and the prediction of cache performance for the access pattern in question. We consider a variety of probability distributions to estimate the data access pattern, and examine a set of learning-based regression techniques to predict the cache hit rate for the access pattern. The predicted cache hit rate is then used to make a decision whether reallocating cache space is needed to meet the QoS requirement for the tenant. Our experimental results with an extensive set of synthetic traces and the YCSB benchmark show that the proposed method consistently optimizes the cache space while satisfying the QoS requirement.

Connected through hyperedge HE-1 (induced by source paper "a hybrid cache architecture for meeting per tenant performance goals in a private cloud"):
  with members:
    - "tinylfu a highly efficient cache admission policy"

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.DC(Distributed, Parallel, and Cluster Computing)