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: "parallel correlation clustering on big graphs"
  Abstract: Given a similarity graph between items, correlation clustering (CC) groups similar items together and dissimilar ones apart. One of the most popular CC algorithms is KwikCluster: an algorithm that serially clusters neighborhoods of vertices, and obtains a 3-approximation ratio. Unfortunately, KwikCluster in practice requires a large number of clustering rounds, a potential bottleneck for large graphs. #R##N#We present C4 and ClusterWild!, two algorithms for parallel correlation clustering that run in a polylogarithmic number of rounds and achieve nearly linear speedups, provably. C4 uses concurrency control to enforce serializability of a parallel clustering process, and guarantees a 3-approximation ratio. ClusterWild! is a coordination free algorithm that abandons consistency for the benefit of better scaling; this leads to a provably small loss in the 3-approximation ratio. #R##N#We provide extensive experimental results for both algorithms, where we outperform the state of the art, both in terms of clustering accuracy and running time. We show that our algorithms can cluster billion-edge graphs in under 5 seconds on 32 cores, while achieving a 15x speedup.

Connected through hyperedge HE-1 (induced by source paper "fair correlation clustering"):
  with members:
    - "coresets for clustering with fairness constraints"
    - "summarizing user generated textual content motivation and methods for fairness in algorithmic summaries"
    - "improved algorithms for correlation clustering with local objectives"
    - "near neighbor who is the fairest of them all"
    - "correlation clustering with adaptive similarity queries"
    - "fair algorithms for clustering"
    - "scalable fair clustering"
    - "achieving fairness in determining medicaid eligibility through fairgroup construction"

Connected through hyperedge HE-2 (induced by source paper "massively parallel benders decomposition for correlation clustering"):
  with members:
    - "accelerating message passing for map with benders decomposition"
    - "exploiting skeletal structure in computer vision annotation with benders decomposition"
    - "a message passing algorithm for the minimum cost multicut problem"
    - "deepercut a deeper stronger and faster multi person pose estimation model"
    - "deepcut joint subset partition and labeling for multi person pose estimation"
    - "efficient decomposition of image and mesh graphs by lifted multicuts"
    - "next generation multicuts for semi planar graphs"
    - "fast planar correlation clustering for image segmentation"

Connected through hyperedge HE-3 (induced by source paper "parallelism in randomized incremental algorithms"):
  with members:
    - "theoretically efficient parallel graph algorithms can be fast and scalable"
    - "parallel shortest paths using radius stepping"
    - "efficient construction of probabilistic tree embeddings"
    - "scalable influence estimation in continuous time diffusion networks"
    - "greedy sequential maximal independent set and matching are parallel on average"

Connected through hyperedge HE-4 (induced by source paper "always lurking understanding and mitigating bias in online human trafficking detection"):
  with members:
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "semantics derived automatically from language corpora contain human like biases"
    - "enriching word vectors with subword information"
    - "character level convolutional networks for text classification"
    - "incremental knowledge base construction using deepdive"
    - "an entity resolution approach to isolate instances of human trafficking online"
    - "renyi divergence and kullback leibler divergence"

Connected through hyperedge HE-5 (induced by source paper "info clustering a mathematical theory for data clustering"):
  with members:
    - "bounds on the communication rate needed to achieve sk capacity in the hypergraphical source model"
    - "duality between feature selection and data clustering"
    - "fairness in communication for omniscience"
    - "incremental and decremental secret key agreement"
    - "on the public communication needed to achieve sk capacity in the multiterminal source model"
    - "iterative merging algorithm for cooperative data exchange"
    - "deep residual learning for image recognition"
    - "the communication complexity of achieving sk capacity in a class of pin models"

Connected through hyperedge HE-6 (induced by source paper "clustering via crowdsourcing"):
  with members:
    - "parallel algorithms for select and partition with noisy comparisons"
    - "fault tolerant entity resolution with the crowd"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "on reverse pinsker inequalities"
    - "exact recovery in the stochastic block model"
    - "crowder crowdsourcing entity resolution"
    - "sorting searching and simulation in the mapreduce framework"
    - "fast clustering using mapreduce"

Connected through hyperedge HE-7 (induced by source paper "delay and cooperation in nonstochastic bandits"):
  with members:
    - "explore no more improved high probability regret bounds for non stochastic bandits"
    - "multi player bandits a musical chairs approach"
    - "on distributed cooperative decision making in multiarmed bandits"
    - "nonstochastic multi armed bandits with graph structured feedback"
    - "distributed online learning in social recommender systems"
    - "online learning under delayed feedback"
    - "distributed online learning via cooperative contextual bandits"
    - "efficient optimal learning for contextual bandits"

Connected through hyperedge HE-8 (induced by source paper "motif clustering and overlapping clustering for social network analysis"):
  with members:
    - "higher order organization of complex networks"
    - "spectral detection on sparse hypergraphs"
    - "near optimal lp rounding algorithm for correlation clustering on complete and complete k partite graphs"
    - "a tutorial on spectral clustering"

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)