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: "small world file sharing communities"
  Abstract: Web caches, content distribution networks, peer-to-peer file sharing networks, distributed file systems, and data grids all have in common that they involve a community of users who generate requests for shared data. In each case, overall system performance can be improved significantly if we can first identify and then exploit interesting structure within a community's access patterns. To this end, we propose a novel perspective on file sharing based on the study of the relationships that form among users based on the files in which they are interested. #R##N#We propose a new structure that captures common user interests in data--the data-sharing graph-- and justify its utility with studies on three data-distribution systems: a high-energy physics collaboration, the Web, and the Kazaa peer-to-peer network. We find small-world patterns in the data-sharing graphs of all three communities. We analyze these graphs and propose some probable causes for these emergent small-world patterns. The significance of small-world patterns is twofold: it provides a rigorous support to intuition and, perhaps most importantly, it suggests ways to design mechanisms that exploit these naturally emerging patterns.

Connected through hyperedge HE-1 (induced by source paper "systems applications of social networks"):
  with members:
    - "a survey of signed network mining in social media"
    - "the architecture of innovation tracking face to face interactions with ubicomp technologies"
    - "social bootstrapping how pinterest and last fm social communities benefit by borrowing links from facebook"
    - "geo spotting mining online location based services for optimal retail store placement"
    - "social turing tests crowdsourcing sybil detection"
    - "explore what if scenarios with song social network write generator"
    - "uncovering social network sybils in the wild"
    - "empirical comparison of algorithms for network community detection"

Connected through hyperedge HE-2 (induced by source paper "content replication in large distributed caches"):
  with members:
    - "content caching and delivery over heterogeneous wireless networks"
    - "serving content with unknown demand the high dimensional regime"
    - "multi level coded caching"
    - "online coded caching"
    - "coded caching with nonuniform demands"
    - "decentralized coded caching attains order optimal memory rate tradeoff"
    - "impact of traffic mix on caching performance in a content centric network"
    - "fundamental limits of caching"

Connected through hyperedge HE-3 (induced by source paper "paging with multiple caches"):
  with members:
    - "adaptive replication in distributed content delivery networks"
    - "serving content with unknown demand the high dimensional regime"
    - "coded caching with nonuniform demands"
    - "decentralized coded caching attains order optimal memory rate tradeoff"
    - "impact of traffic mix on caching performance in a content centric network"
    - "fundamental limits of caching"
    - "competitive paging algorithms"

Connected through hyperedge HE-4 (induced by source paper "reputation aggregation in peer to peer network using differential gossip algorithm"):
  with members:
    - "trust estimation in peer to peer network using blue"

Connected through hyperedge HE-5 (induced by source paper "serving content with unknown demand the high dimensional regime"):
  with members:
    - "adaptive replication in distributed content delivery networks"
    - "impact of traffic mix on caching performance in a content centric network"

Connected through hyperedge HE-6 (induced by source paper "identifying high betweenness centrality nodes in large social networks"):
  with members:
    - "forensic analysis of phone call networks"
    - "fast approximation of centrality"

Connected through hyperedge HE-7 (induced by source paper "mpo an efficient and low cost peer to peer overlay for autonomic communications"):
  with members:
    - "efficiency and nash equilibria in a scrip system for p2p networks"

Connected through hyperedge HE-8 (induced by source paper "ad hoc limited scale free models for unstructured peer to peer networks"):
  with members:
    - "scale free overlay topologies with hard cutoffs for unstructured peer to peer networks"
    - "a statistical physics perspective on web growth"

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)