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: "gunrock a high performance graph processing library on the gpu"
  Abstract: For large-scale graph analytics on the GPU, the irregularity of data access and control flow, and the complexity of programming GPUs have been two significant challenges for developing a programmable high-performance graph library. "Gunrock", our graph-processing system designed specifically for the GPU, uses a high-level, bulk-synchronous, data-centric abstraction focused on operations on a vertex or edge frontier. Gunrock achieves a balance between performance and expressiveness by coupling high performance GPU computing primitives and optimization strategies with a high-level programming model that allows programmers to quickly develop new graph primitives with small code size and minimal GPU programming knowledge. We evaluate Gunrock on five key graph primitives and show that Gunrock has on average at least an order of magnitude speedup over Boost and PowerGraph, comparable performance to the fastest GPU hardwired primitives, and better performance than any other GPU high-level graph library.

Connected through hyperedge HE-1 (induced by source paper "hygcn a gcn accelerator with hybrid architecture"):
  with members:
    - "fast graph representation learning with pytorch geometric"
    - "a comprehensive survey on graph neural networks"
    - "pytorch biggraph a large scale graph embedding system"
    - "graph neural networks a review of methods and applications"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hierarchical graph representation learning with differentiable pooling"
    - "representation learning on graphs with jumping knowledge networks"
    - "deep learning on graphs a survey"

Connected through hyperedge HE-2 (induced by source paper "characterizing and understanding gcns on gpu"):
  with members:
    - "fast graph representation learning with pytorch geometric"
    - "pytorch biggraph a large scale graph embedding system"
    - "how powerful are graph neural networks"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "a survey on graph processing accelerators challenges and opportunities"):
  with members:
    - "flashabacus a self governing flash based accelerator for low power systems"
    - "relational inductive biases deep learning and graph networks"
    - "struc2vec learning node representations from structural identity"
    - "in datacenter performance analysis of a tensor processing unit"
    - "democratizing design for future computing platforms"
    - "graph2vec learning distributed representations of graphs"
    - "graphr accelerating graph processing using reram"
    - "novel graph processor architecture prototype system and results"

Connected through hyperedge HE-4 (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"
    - "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"
    - "fast exact shortest path distance queries on large networks by pruned landmark labeling"

Connected through hyperedge HE-5 (induced by source paper "jgrapht a java library for graph data structures and algorithms"):
  with members:
    - "graphblast a high performance linear algebra based graph framework on the gpu"
    - "mathematical foundations of the graphblas"
    - "tight lower and upper bounds for the complexity of canonical colour refinement"
    - "networkit a tool suite for large scale complex network analysis"
    - "pagerank beyond the web"
    - "malware classification based on call graph clustering"
    - "listing all maximal cliques in sparse graphs in near optimal time"

Connected through hyperedge HE-6 (induced by source paper "prioritygraph a unified programming model for optimizing ordered graph algorithms"):
  with members:
    - "start late or finish early a distributed graph processing system with redundancy reduction"
    - "distributionally linearizable data structures"
    - "gunrock gpu graph analytics"
    - "the gap benchmark suite"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-7 (induced by source paper "gsi gpu friendly subgraph isomorphism"):
  with members:
    - "optimizing subgraph queries by combining binary and worst case optimal joins"
    - "taming subgraph isomorphism for rdf query processing"
    - "efficient subgraph matching on billion node graphs"
    - "a new data layout for set intersection on gpus"
    - "semantics and complexity of sparql"

Connected through hyperedge HE-8 (induced by source paper "an adaptive load balancer for graph analytical applications on gpus"):
  with members:
    - "gunrock gpu graph analytics"
    - "dynamic load balancing strategies for graph applications on gpus"
    - "multi gpu graph analytics"
    - "parallel breadth first search on distributed memory systems"

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)