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: "simple distributed weighted matchings"
  Abstract: Wattenhofer et al. [WW04] derive a complicated distributed algorithm to compute a weighted matching of an arbitrary weighted graph, that is at most a factor 5 away from the maximum weighted matching of that graph. We show that a variant of the obvious sequential greedy algorithm [Pre99], that computes a weighted matching at most a factor 2 away from the maximum, is easily distributed. This yields the best known distributed approximation algorithm for this problem so far.

Connected through hyperedge HE-1 (induced by source paper "cooperative caching in fog radio access networks a graph based approach"):
  with members:
    - "user preference learning based edge caching for fog radio access network"
    - "learning based caching in cloud aided wireless networks"
    - "context aware proactive content caching with service differentiation in wireless networks"
    - "fog computing based radio access networks issues and challenges"
    - "mobility aware caching in d2d networks"
    - "dynamic clustering and user association in wireless small cell networks with social considerations"
    - "cooperative caching and transmission design in cluster centric small cell networks"
    - "content aware user clustering and caching in wireless small cell networks"

Connected through hyperedge HE-2 (induced by source paper "euclidean tsp motorcycle graphs and other new applications of nearest neighbor chains"):
  with members:
    - "faster algorithms for some optimization problems on collinear points"
    - "dynamic planar voronoi diagrams for general distance functions and their algorithmic applications"
    - "the approximation ratio of the greedy algorithm for the metric traveling salesman problem"
    - "a stable marriage requires communication"
    - "general auction mechanism for search advertising"

Connected through hyperedge HE-3 (induced by source paper "serenade a parallel randomized algorithm suite for crossbar scheduling in input queued switches"):
  with members:
    - "parallel scheduling algorithm based on complex coloring for input queued switches"
    - "delay based back pressure scheduling in multihop wireless networks"
    - "node weighted scheduling"
    - "max product for maximum weight matching convergence correctness and lp duality"

Connected through hyperedge HE-4 (induced by source paper "distributed vertex cover algorithms for wireless sensor networks"):
  with members:
    - "cluster based load balancing algorithms for grids"
    - "a simple local 3 approximation algorithm for vertex cover"

Connected through hyperedge HE-5 (induced by source paper "efficient parallel and external matching"):
  with members:
    - "greedy sequential maximal independent set and matching are parallel on average"
    - "sorting searching and simulation in the mapreduce framework"
    - "engineering a scalable high quality graph partitioner"

Connected through hyperedge HE-6 (induced by source paper "delay based back pressure scheduling in multihop wireless networks"):
  with members:
    - "throughput optimal opportunistic scheduling in the presence of flow level dynamics"

Connected through hyperedge HE-7 (induced by source paper "optimal resource allocation and relay selection in bandwidth exchange based cooperative forwarding"):
  with members:
    - "transmit signal and bandwidth optimization in multiple antenna relay channels"

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)