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: "near data scheduling for data centers with multiple levels of data locality"
  Abstract: Data locality is a fundamental issue for data-parallel applications. Considering MapReduce in Hadoop, the map task scheduling part requires an efficient algorithm which takes data locality into consideration; otherwise, the system may become unstable under loads inside the system's capacity region and jobs may experience longer completion times which are not of interest. The data chunk needed for any map task can be in memory, on a local disk, in a local rack, in the same cluster or even in another data center. Hence, unless there has been much work on improving the speed of data center networks, different levels of service rates still exist for a task depending on where its data chunk is saved and from which server it receives service. Most of the theoretical work on load balancing is for systems with two levels of data locality including the Pandas algorithm by Xie et al. and the JSQ-MW algorithm by Wang et al., where the former is both throughput and heavy-traffic optimal, while the latter is only throughput optimal, but heavy-traffic optimal in only a special traffic load. We show that an extension of the JSQ-MW algorithm for a system with thee levels of data locality is throughput optimal, but not heavy-traffic optimal for all loads, only for a special traffic scenario. Furthermore, we show that the Pandas algorithm is not even throughput optimal for a system with three levels of data locality. We then propose a novel algorithm, Balanced-Pandas, which is both throughput and heavy-traffic optimal. To the best of our knowledge, this is the first theoretical work on load balancing for a system with more than two levels of data locality. This is more challenging than two levels of data locality as a dilemma between performance and throughput emerges.

Connected through hyperedge HE-1 (induced by source paper "risk averse explore then commit algorithms for finite time bandits"):
  with members:
    - "risk aware multi armed bandits using conditional value at risk"
    - "blind gb pandas a blind throughput optimal load balancing algorithm for affinity scheduling"
    - "gb pandas throughput and heavy traffic optimality analysis for affinity scheduling"
    - "distributed user association in energy harvesting dense small cell networks a mean field multi armed bandit approach"
    - "stochastic multi armed bandits in constant space"
    - "risk averse multi armed bandit problems under mean variance measure"
    - "a survey on contextual multi armed bandits"
    - "exploration vs exploitation vs safety risk averse multi armed bandits"

Connected through hyperedge HE-2 (induced by source paper "the power of d choices in scheduling for data centers with heterogeneous servers"):
  with members:
    - "a game theoretical framework for the evaluation of unmanned aircraft systems airspace integration concepts"
    - "blind gb pandas a blind throughput optimal load balancing algorithm for affinity scheduling"
    - "gb pandas throughput and heavy traffic optimality analysis for affinity scheduling"
    - "a mobile geo communication dataset for physiology aware dash in rural ambulance transport"
    - "affinity scheduling and the applications on data center scheduling with data locality"
    - "counter factual reinforcement learning how to model decision makers that anticipate the future"

Connected through hyperedge HE-3 (induced by source paper "blind gb pandas a blind throughput optimal load balancing algorithm for affinity scheduling"):
  with members:
    - "gb pandas throughput and heavy traffic optimality analysis for affinity scheduling"

Connected through hyperedge HE-4 (induced by source paper "quickcast fast and efficient inter datacenter transfers using forwarding tree cohorts"):
  with members:
    - "datacenter traffic control understanding techniques and tradeoffs"
    - "gb pandas throughput and heavy traffic optimality analysis for affinity scheduling"
    - "dccast efficient point to multipoint transfers across datacenters"
    - "ddccast meeting point to multipoint transfer deadlines across datacenters using alap scheduling policy"
    - "multicast traffic engineering for software defined networks"

Connected through hyperedge HE-5 (induced by source paper "estimating the rating of reviewers based on the text"):
  with members:
    - "affinity scheduling and the applications on data center scheduling with data locality"
    - "twitter opinion topic model extracting product opinions from tweets by leveraging hashtags and sentiment lexicon"
    - "how opinions are received by online communities a case study on amazon com helpfulness votes"
    - "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales"

Connected through hyperedge HE-6 (induced by source paper "datacenter traffic control understanding techniques and tradeoffs"):
  with members:
    - "gb pandas throughput and heavy traffic optimality analysis for affinity scheduling"
    - "rcd rapid close to deadline scheduling for datacenter networks"
    - "low latency datacenter networking a short survey"

Connected through hyperedge HE-7 (induced by source paper "gb pandas throughput and heavy traffic optimality analysis for affinity scheduling"):
  with members:
    - "dccast efficient point to multipoint transfers across datacenters"
    - "dcroute speeding up inter datacenter traffic allocation while guaranteeing deadlines"

Connected through hyperedge HE-8 (induced by source paper "analyzing self driving cars on twitter"):
  with members:
    - "gb pandas throughput and heavy traffic optimality analysis for affinity scheduling"

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)