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: "thrill high performance algorithmic distributed batch data processing with c"
  Abstract: We present the design and a first performance evaluation of Thrill -- a prototype of a general purpose big data processing framework with a convenient data-flow style programming interface. Thrill is somewhat similar to Apache Spark and Apache Flink with at least two main differences. First, Thrill is based on C++ which enables performance advantages due to direct native code compilation, a more cache-friendly memory layout, and explicit memory management. In particular, Thrill uses template meta-programming to compile chains of subsequent local operations into a single binary routine without intermediate buffering and with minimal indirections. Second, Thrill uses arrays rather than multisets as its primary data structure which enables additional operations like sorting, prefix sums, window scans, or combining corresponding fields of several arrays (zipping). We compare Thrill with Apache Spark and Apache Flink using five kernels from the HiBench suite. Thrill is consistently faster and often several times faster than the other frameworks. At the same time, the source codes have a similar level of simplicity and abstraction

Connected through hyperedge HE-1 (induced by source paper "recent advances in scalable network generation"):
  with members:
    - "efficiently generating geometric inhomogeneous and hyperbolic random graphs"
    - "exploring randomly wired neural networks for image recognition"
    - "parallel weighted random sampling"
    - "linear work generation of r mat graphs"
    - "egbter capturing degree distribution clustering coefficients and community structure in a single random graph model"
    - "multiscale planar graph generation"
    - "parallel and i o efficient randomisation of massive networks using global curveball trades"
    - "efficient parallel random sampling vectorized cache efficient and online"

Connected through hyperedge HE-2 (induced by source paper "connecting mapreduce computations to realistic machine models"):
  with members:
    - "communication efficient algorithms for top k selection problems"
    - "bsp vs mapreduce"
    - "sorting searching and simulation in the mapreduce framework"

Connected through hyperedge HE-3 (induced by source paper "air a light weight yet high performance dataflow engine based on asynchronous iterative routing"):
  with members:
    - "benchmarking distributed stream data processing systems"

Connected through hyperedge HE-4 (induced by source paper "the coming age of pervasive data processing"):
  with members:
    - "in datacenter performance analysis of a tensor processing unit"
    - "execution templates caching control plane decisions for strong scaling of data analytics"
    - "tensorflow a system for large scale machine learning"
    - "mllib machine learning in apache spark"
    - "deep speech 2 end to end speech recognition in english and mandarin"

Connected through hyperedge HE-5 (induced by source paper "parallel weighted random sampling"):
  with members:
    - "efficient parallel random sampling vectorized cache efficient and online"
    - "improved parallel construction of wavelet trees and rank select structures"

Connected through hyperedge HE-6 (induced by source paper "the isti rapid response on exploring cloud computing 2018"):
  with members:
    - "high performance python for direct numerical simulations of turbulent flows"
    - "convolutional radio modulation recognition networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-7 (induced by source paper "relational equivalence proofs between imperative and mapreduce algorithms"):
  with members:
    - "proving equivalence between imperative and mapreduce implementations using program transformations"
    - "an executable sequential specification for spark aggregation"
    - "the commutativity problem of the mapreduce framework a transducer based approach"

Connected through hyperedge HE-8 (induced by source paper "distributed graph clustering using modularity and map equation"):
  with members:
    - "engineering parallel algorithms for community detection in massive networks"
    - "i o efficient generation of massive graphs following the lfr benchmark"

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)