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: "distributed algorithms for finding local clusters using heat kernel pagerank"
  Abstract: A distributed algorithm performs local computations on pieces of input and communicates the results through given communication links. When processing a massive graph in a distributed algorithm, local outputs must be configured as a solution to a graph problem without shared memory and with few rounds of communication. In this paper we consider the problem of computing a local cluster in a massive graph in the distributed setting. Computing local clusters are of certain application-specific interests, such as detecting communities in social networks or groups of interacting proteins in biological networks. When the graph models the computer network itself, detecting local clusters can help to prevent communication bottlenecks. We give a distributed algorithm that computes a local cluster in time that depends only logarithmically on the size of the graph in the CONGEST model. In particular, when the value of the optimal local cluster is known, the algorithm runs in time entirely independent of the size of the graph and depends only on error bounds for approximation. We also show that the local cluster problem can be computed in the k-machine distributed model in sublinear time. The speedup of our local cluster algorithms is mainly due to the use of our distributed algorithm for heat kernel pagerank.

Connected through hyperedge HE-1 (induced by source paper "efficient estimation of heat kernel pagerank for local clustering"):
  with members:
    - "capacity releasing diffusion for speed and locality"
    - "parallel local graph clustering"
    - "personalized pagerank estimation and search a bidirectional approach"
    - "a simple and strongly local flow based method for cut improvement"
    - "fast bidirectional probability estimation in markov models"
    - "computing heat kernel pagerank and a local clustering algorithm"
    - "heat kernel based community detection"
    - "flow based algorithms for local graph clustering"

Connected through hyperedge HE-2 (induced by source paper "parallel local graph clustering"):
  with members:
    - "a simple and strongly local flow based method for cut improvement"
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "computing heat kernel pagerank and a local clustering algorithm"
    - "improved cheeger s inequality and analysis of local graph partitioning using vertex expansion and expansion profile"
    - "heat kernel based community detection"
    - "flow based algorithms for local graph clustering"
    - "approximating the expansion profile and almost optimal local graph clustering"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-3 (induced by source paper "on the distributed complexity of large scale graph computations"):
  with members:
    - "mst in o 1 rounds of the congested clique"
    - "triangle finding and listing in congest networks"
    - "near optimal clustering in the k machine model"
    - "tight bounds for distributed graph computations"
    - "algebraic methods in the congested clique"
    - "fast distributed algorithms for connectivity and mst in large graphs"
    - "fast distributed pagerank computation"
    - "distributed computation of large scale graph problems"

Connected through hyperedge HE-4 (induced by source paper "fast distributed algorithms for connectivity and mst in large graphs"):
  with members:
    - "near optimal clustering in the k machine model"
    - "tight bounds for distributed graph computations"
    - "on the distributed complexity of large scale graph computations"
    - "a time and message optimal distributed algorithm for minimum spanning trees"
    - "super fast mst algorithms in the congested clique using o m messages"
    - "algebraic methods in the congested clique"
    - "construction and impromptu repair of an mst in a distributed network with o m communication"
    - "computing exact distances in the congested clique"

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)