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: "waca a hierarchical weighted clustering algorithm optimized for mobile hybrid networks"
  Abstract: Clustering techniques create hierarchal network structures, called clusters, on an otherwise flat network. In a dynamic environment-in terms of node mobility as well as in terms of steadily changing device parameters-the clusterhead election process has to be re-invoked according to a suitable update policy. Cluster re-organization causes additional message exchanges and computational complexity and it execution has to be optimized. Our investigations focus on the problem of minimizing clusterhead re-elections by considering stability criteria. These criteria are based on topological characteristics as well as on device parameters. This paper presents a weighted clustering algorithm optimized to avoid needless clusterhead re-elections for stable clusters in mobile ad-hoc networks. The proposed localized algorithm deals with mobility, but does not require geographical, speed or distances information.

Connected through hyperedge HE-1 (induced by source paper "vbca a virtual forces clustering algorithm for autonomous aerial drone systems"):
  with members:
    - "a networked swarm model for uav deployment in the assessment of forest environments"

Connected through hyperedge HE-2 (induced by source paper "self organization of nodes using bio inspired techniques for achieving small world properties"):
  with members:
    - "self organization of wireless ad hoc networks as small worlds using long range directional beams"
    - "small worlds strong clustering in wireless networks"

Connected through hyperedge HE-3 (induced by source paper "achieving small world properties using bio inspired techniques in wireless networks"):
  with members:
    - "self organization of wireless ad hoc networks as small worlds using long range directional beams"
    - "small worlds strong clustering in wireless networks"

Connected through hyperedge HE-4 (induced by source paper "localized support for injection point election in hybrid networks"):
  with members:
    - "a communication model for adaptive service provisioning in hybrid wireless 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.DC(Distributed, Parallel, and Cluster Computing)