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: "bigdatabench a big data benchmark suite from web search engines"
  Abstract: This paper presents our joint research efforts on big data benchmarking with several industrial partners. Considering the complexity, diversity, workload churns, and rapid evolution of big data systems, we take an incremental approach in big data benchmarking. For the first step, we pay attention to search engines, which are the most important domain in Internet services in terms of the number of page views and daily visitors. However, search engine service providers treat data, applications, and web access logs as business confidentiality, which prevents us from building benchmarks. To overcome those difficulties, with several industry partners, we widely investigated the open source solutions in search engines, and obtained the permission of using anonymous Web access logs. Moreover, with two years' great efforts, we created a sematic search engine named ProfSearch (available from this http URL). These efforts pave the path for our big data benchmark suite from search engines---BigDataBench, which is released on the web page (this http URL). We report our detailed analysis of search engine workloads, and present our benchmarking methodology. An innovative data generation methodology and tool are proposed to generate scalable volumes of big data from a small seed of real data, preserving semantics and locality of data. Also, we preliminarily report two case studies using BigDataBench for both system and architecture researches.

Connected through hyperedge HE-1 (induced by source paper "improving spark application throughput via memory aware task co location a mixture of experts approach"):
  with members:
    - "performance characterization of in memory data analytics on a modern cloud server"
    - "mli an api for distributed machine learning"

Connected through hyperedge HE-2 (induced by source paper "benchmarking distributed stream processing platforms for iot applications"):
  with members:
    - "holistic measures for evaluating prediction models in smart grids"

Connected through hyperedge HE-3 (induced by source paper "understanding big data analytic workloads on modern processors"):
  with members:
    - "bigdatabench a big data benchmark suite from internet services"
    - "bigop generating comprehensive big data workloads as a benchmarking framework"
    - "characterizing and subsetting big data workloads"
    - "characterizing data analysis workloads in data centers"
    - "high volume throughput computing identifying and characterizing throughput oriented workloads in data centers"

Connected through hyperedge HE-4 (induced by source paper "benchmarking big data systems state of the art and future directions"):
  with members:
    - "bigdatabench a big data benchmark suite from internet services"
    - "characterizing and subsetting big data workloads"
    - "characterizing data analysis workloads in data centers"
    - "interactive analytical processing in big data systems a cross industry study of mapreduce workloads"

Connected through hyperedge HE-5 (induced by source paper "bigdatabench a big data benchmark suite from internet services"):
  with members:
    - "bdgs a scalable big data generator suite in big data benchmarking"
    - "characterizing data analysis workloads in data centers"
    - "high volume throughput computing identifying and characterizing throughput oriented workloads in data centers"

Connected through hyperedge HE-6 (induced by source paper "the implications of diverse applications and scalable data sets in benchmarking big data systems"):
  with members:
    - "characterizing data analysis workloads in data centers"
    - "high volume throughput computing identifying and characterizing throughput oriented workloads in data centers"
    - "in cloud can scientific communities benefit from the economies of scale"

Connected through hyperedge HE-7 (induced by source paper "axpue application level metrics for power usage effectiveness in data centers"):
  with members:
    - "the implications of diverse applications and scalable data sets in benchmarking big data systems"
    - "high volume throughput computing identifying and characterizing throughput oriented workloads in data centers"

Connected through hyperedge HE-8 (induced by source paper "the implications from benchmarking three big data systems"):
  with members:
    - "the implications of diverse applications and scalable data sets in benchmarking big data systems"
    - "characterizing data analysis workloads in data centers"

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)