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: "graphlab a new framework for parallel machine learning"
  Abstract: Designing and implementing efficient, provably correct parallel machine learning (ML) algorithms is challenging. Existing high-level parallel abstractions like MapReduce are insufficiently expressive while low-level tools like MPI and Pthreads leave ML experts repeatedly solving the same design challenges. By targeting common patterns in ML, we developed GraphLab, which improves upon abstractions like MapReduce by compactly expressing asynchronous iterative algorithms with sparse computational dependencies while ensuring data consistency and achieving a high degree of parallel performance. We demonstrate the expressiveness of the GraphLab framework by designing and implementing parallel versions of belief propagation, Gibbs sampling, Co-EM, Lasso and Compressed Sensing. We show that using GraphLab we can achieve excellent parallel performance on large scale real-world problems.

Connected through hyperedge HE-1 (induced by source paper "relaxed scheduling for scalable belief propagation"):
  with members:
    - "message scheduling for performant many core belief propagation"
    - "relaxed schedulers can efficiently parallelize iterative algorithms"
    - "theoretically efficient parallel graph algorithms can be fast and scalable"
    - "distributionally linearizable data structures"
    - "parallelism in randomized incremental algorithms"
    - "the power of choice in priority scheduling"
    - "the lock free k lsm relaxed priority queue"
    - "greedy sequential maximal independent set and matching are parallel on average"

Connected through hyperedge HE-2 (induced by source paper "domain specialized cache management for graph analytics"):
  with members:
    - "a closer look at lightweight graph reordering"
    - "low latency graph streaming using compressed purely functional trees"
    - "the gap benchmark suite"
    - "fast computation of empirically tight bounds for the diameter of massive graphs"

Connected through hyperedge HE-3 (induced by source paper "a closer look at lightweight graph reordering"):
  with members:
    - "domain specialized cache management for graph analytics"
    - "the gap benchmark suite"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"
    - "fast computation of empirically tight bounds for the diameter of massive graphs"

Connected through hyperedge HE-4 (induced by source paper "ripple a practical declarative programming framework for serverless compute"):
  with members:
    - "occupy the cloud distributed computing for the 99"
    - "tensorflow a system for large scale machine learning"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"

Connected through hyperedge HE-5 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism"):
  with members:
    - "storing and querying large scale spatio temporal graphs with high throughput edge insertions"
    - "parallel batch dynamic graph connectivity"
    - "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries"
    - "graph processing on fpgas taxonomy survey challenges"
    - "slim fly a cost effective low diameter network topology"
    - "red blue pebbling revisited near optimal parallel matrix matrix multiplication"
    - "dynamic algorithms for the massively parallel computation model"
    - "fatpaths routing in supercomputers data centers and clouds with low diameter networks when shortest paths fall short"

Connected through hyperedge HE-6 (induced by source paper "processing data where it makes sense enabling in memory computation"):
  with members:
    - "enabling the adoption of processing in memory challenges mechanisms future research directions"
    - "architectural techniques for improving nand flash memory reliability"
    - "what your dram power models are not telling you lessons from a detailed experimental study"
    - "techniques for shared resource management in systems with throughput processors"
    - "errors in flash memory based solid state drives analysis mitigation and recovery"
    - "the rowhammer problem and other issues we may face as memory becomes denser"
    - "error characterization mitigation and recovery in flash memory based solid state drives"
    - "using ecc dram to adaptively increase memory capacity"

Connected through hyperedge HE-7 (induced by source paper "graph processing on fpgas taxonomy survey challenges"):
  with members:
    - "slim fly a cost effective low diameter network topology"
    - "a modular benchmarking infrastructure for high performance and reproducible deep learning"
    - "survey and taxonomy of lossless graph compression and space efficient graph representations"
    - "demystifying parallel and distributed deep learning an in depth concurrency analysis"
    - "high level programming abstractions for distributed graph processing"
    - "an efficient dispatcher for large scale graphprocessing on opencl based fpgas"
    - "an efficient graph accelerator with parallel data conflict management"
    - "mathematical foundations of the graphblas"

Connected through hyperedge HE-8 (induced by source paper "pruned landmark labeling meets vertex centric computation a surprisingly happy marriage"):
  with members:
    - "theoretically efficient parallel graph algorithms can be fast and scalable"
    - "gunrock a high performance graph processing library on the gpu"
    - "route planning in transportation networks"
    - "hop doubling label indexing for point to point distance querying on scale free networks"
    - "nscale neighborhood centric large scale graph analytics in the cloud"
    - "tree decompositions and social graphs"
    - "simple fast and scalable reachability oracle"
    - "hub accelerator fast and exact shortest path computation in large social networks"

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.LG(Machine Learning)