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: "finding connected components on map reduce in logarithmic rounds"
  Abstract: Given a large graph G = (V,E) with millions of nodes and edges, how do we compute its connected components efficiently? Recent work addresses this problem in map-reduce, where a fundamental trade-off exists between the number of map-reduce rounds and the communication of each round. Denoting d the diameter of the graph, and n the number of nodes in the largest component, all prior map-reduce techniques either require d rounds, or require about n|V| + |E| communication per round. We propose two randomized map-reduce algorithms -- (i) Hash-Greater-To-Min, which provably requires at most 3log(n) rounds with high probability, and at most 2(|V| + |E|) communication per round, and (ii) Hash-to-Min, which has a worse theoretical complexity, but in practice completes in at most 2log(d) rounds and 3(|V| + |E|) communication per rounds. #R##N#Our techniques for connected components can be applied to clustering as well. We propose a novel algorithm for agglomerative single linkage clustering in map-reduce. This is the first algorithm that can provably compute a clustering in at most O(log(n)) rounds, where n is the size of the largest cluster. We show the effectiveness of all our algorithms through detailed experiments on large synthetic as well as real-world datasets.

Connected through hyperedge HE-1 (induced by source paper "equivalence classes and conditional hardness in massively parallel computations"):
  with members:
    - "walking randomly massively and efficiently"
    - "log diameter rounds algorithms for 2 vertex and 2 edge connectivity"
    - "dynamic algorithms for the massively parallel computation model"
    - "a conditional lower bound on graph connectivity in mapreduce"
    - "efficient circuit simulation in mapreduce"
    - "exponentially faster massively parallel maximal matching"
    - "parallel batch dynamic graphs algorithms and lower bounds"
    - "massively parallel algorithms for finding well connected components in sparse graphs"

Connected through hyperedge HE-2 (induced by source paper "dynamic algorithms for the massively parallel computation model"):
  with members:
    - "fully dynamic maximal independent set with sublinear update time"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"
    - "connected components at scale via local contractions"
    - "a deamortization approach for dynamic spanner and dynamic maximal matching"
    - "coresets meet edcs algorithms for matching and vertex cover on massive graphs"
    - "dynamic matching reducing integral algorithms to approximately maximal fractional algorithms"
    - "round compression for parallel matching algorithms"
    - "dynamic minimum spanning forest with subpolynomial worst case update time"

Connected through hyperedge HE-3 (induced by source paper "autoer automated entity resolution using generative modelling"):
  with members:
    - "reuse and adaptation for entity resolution through transfer learning"
    - "snorkel rapid training data creation with weak supervision"
    - "inverse covariance estimation for high dimensional data in linear time and space spectral methods for riccati and sparse models"
    - "crowder crowdsourcing entity resolution"
    - "projected subgradient methods for learning sparse gaussians"

Connected through hyperedge HE-4 (induced by source paper "fastsv a distributed memory connected component algorithm with fast convergence"):
  with members:
    - "graphblast a high performance linear algebra based graph framework on the gpu"
    - "composing optimization techniques for vertex centric graph processing via communication channels"
    - "an adaptive parallel algorithm for computing connected components"
    - "a work efficient parallel sparse matrix sparse vector multiplication algorithm"
    - "parallel breadth first search on distributed memory systems"

Connected through hyperedge HE-5 (induced by source paper "randomized experimental design via geographic clustering"):
  with members:
    - "distributed balanced partitioning via linear embedding"
    - "graph cluster randomization network exposure to multiple universes"

Connected through hyperedge HE-6 (induced by source paper "massively parallel algorithms for finding well connected components in sparse graphs"):
  with members:
    - "matching and mis for uniformly sparse graphs in the low memory mpc model"
    - "sparsifying distributed algorithms with ramifications in massively parallel computation and centralized local computation"
    - "brief announcement semi mapreduce meets congested clique"
    - "breaking the linear memory barrier in mpc fast mis on trees with n e memory per machine"
    - "the complexity of delta 1 coloring incongested clique massively parallel computation and centralized local computation"
    - "mis in the congested clique model in o log log δ rounds"
    - "round compression for parallel graph algorithms in strongly sublinear space"
    - "mst in o 1 rounds of the congested clique"

Connected through hyperedge HE-7 (induced by source paper "random sampling applied to the mst problem in the node congested clique model"):
  with members:
    - "mst in o 1 rounds of the congested clique"
    - "round compression for parallel matching algorithms"
    - "deterministic mst sparsification in the congested clique"
    - "distributed computation of large scale graph problems"
    - "optimal deterministic routing and sorting on the congested clique"

Connected through hyperedge HE-8 (induced by source paper "on efficiently detecting overlapping communities over distributed dynamic graphs"):
  with members:
    - "labelrankt incremental community detection in dynamic networks via label propagation"
    - "towards linear time overlapping community detection in social networks"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social 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.DS(Data Structures and Algorithms)