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: "mr simlab scalable subgraph selection with label similarity for big data"
  Abstract: With the increasing size and complexity of available databases, existing machine learning and data mining algorithms are facing a scalability challenge. In many applications, the number of features describing the data could be extremely high. This hinders or even could make any further exploration infeasible. In fact, many of these features are redundant or simply irrelevant. Hence, feature selection plays a key role in helping to overcome the problem of information overload especially in big data applications. Since many complex datasets could be modeled by graphs of interconnected labeled elements, in this work, we are particularly interested in feature selection for subgraph patterns. In this paper, we propose MR-SimLab, a MapReduce-based approach for subgraph selection from large input subgraph sets. In many applications, it is easy to compute pairwise similarities between labels of the graph nodes. Our approach leverages such rich information to measure an approximate subgraph matching by aggre-gating the elementary label similarities between the matched nodes. Based on the aggregated similarity scores, our approach selects a small subset of informative representative subgraphs. We provide a distributed implementation of our algorithm on top of the MapReduce framework that optimizes the computational efficiency of our approach for big data applications. We experimentally evaluate MR-SimLab on real datasets. The obtained results show that our approach is scalable and that the selected subgraphs are informative.

Connected through hyperedge HE-1 (induced by source paper "an experimental survey on big data frameworks"):
  with members:
    - "bladyg a graph processing framework for large dynamic graphs"
    - "big graph mining frameworks and techniques"
    - "a tale of two data intensive paradigms applications abstractions and architectures"
    - "mli an api for distributed machine learning"
    - "k core decomposition of internet graphs hierarchies self similarity and measurement biases"

Connected through hyperedge HE-2 (induced by source paper "big data frameworks a comparative study"):
  with members:
    - "bladyg a graph processing framework for large dynamic graphs"
    - "big graph mining frameworks and techniques"
    - "a tale of two data intensive paradigms applications abstractions and architectures"
    - "mli an api for distributed machine learning"
    - "k core decomposition of internet graphs hierarchies self similarity and measurement biases"

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.CE(Computational Engineering, Finance, and Science)