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 local graph clustering"
  Abstract: Graph clustering has many important applications in computing, but due to growing sizes of graphs, even traditionally fast clustering methods such as spectral partitioning can be computationally expensive for real-world graphs of interest. Motivated partly by this, so-called local algorithms for graph clustering have received significant interest due to the fact that they can find good clusters in a graph with work proportional to the size of the cluster rather than that of the entire graph. This feature has proven to be crucial in making such graph clustering and many of its downstream applications efficient in practice. While local clustering algorithms are already faster than traditional algorithms that touch the entire graph, they are sequential and there is an opportunity to make them even more efficient via parallelization. In this paper, we show how to parallelize many of these algorithms in the shared-memory multicore setting, and we analyze the parallel complexity of these algorithms. We present comprehensive experiments on large-scale graphs showing that our parallel algorithms achieve good parallel speedups on a modern multicore machine, thus significantly speeding up the analysis of local graph clusters in the very large-scale setting.

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"
    - "personalized pagerank estimation and search a bidirectional approach"
    - "a simple and strongly local flow based method for cut improvement"
    - "distributed algorithms for finding local clusters using heat kernel pagerank"
    - "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 "statistical guarantees for local graph clustering"):
  with members:
    - "flow based local graph clustering with better seed set inclusion"
    - "capacity releasing diffusion for speed and locality"
    - "an optimization approach to locally biased graph algorithms"
    - "a simple and strongly local flow based method for cut improvement"
    - "heat kernel based community detection"
    - "flow based algorithms for local graph clustering"
    - "on semidefinite relaxations for the block model"
    - "exact recovery in the stochastic block model"

Connected through hyperedge HE-3 (induced by source paper "low latency graph streaming using compressed purely functional trees"):
  with members:
    - "multiversion concurrency with bounded delay and precise garbage collection"
    - "theoretically efficient parallel graph algorithms can be fast and scalable"
    - "pam parallel augmented maps"
    - "an introduction to temporal graphs an algorithmic perspective"
    - "storing and analyzing historical graph data at scale"
    - "the gap benchmark suite"
    - "efficient snapshot retrieval over historical graph data"

Connected through hyperedge HE-4 (induced by source paper "viewing computer science through citation analysis salton and bergmark redux"):
  with members:
    - "from louvain to leiden guaranteeing well connected communities"
    - "which type of citation analysis generates the most accurate taxonomy of scientific and technical knowledge"
    - "role of interdisciplinarity in computer sciences quantification impact and life trajectory"
    - "clustering scientific publications based on citation relations a systematic comparison of different methods"
    - "analysis of network clustering algorithms and cluster quality metrics at scale"
    - "large scale analysis of the accuracy of the journal classification systems of web of science and scopus"
    - "a new methodology for constructing a publication level classification system of science"

Connected through hyperedge HE-5 (induced by source paper "gpop a cache and work efficient framework for graph processing over partitions"):
  with members:
    - "partitionedvc partitioned external memory graph analytics framework for ssds"
    - "graphit a high performance dsl for graph analytics"
    - "accelerating pagerank using partition centric processing"
    - "a work efficient parallel sparse matrix sparse vector multiplication algorithm"
    - "goffish a sub graph centric framework for large scale graph analytics"
    - "geotagging one hundred million twitter accounts with total variation minimization"
    - "evolution of social attribute networks measurements modeling and implications using google"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

Connected through hyperedge HE-6 (induced by source paper "lasagne locality and structure aware graph node embedding"):
  with members:
    - "struc2vec learning node representations from structural identity"
    - "neural message passing for quantum chemistry"
    - "gated graph sequence neural networks"
    - "node2vec scalable feature learning for networks"
    - "semi supervised classification with graph convolutional networks"
    - "revisiting semi supervised learning with graph embeddings"
    - "parallelizing word2vec in shared and distributed memory"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-7 (induced by source paper "capacity releasing diffusion for speed and locality"):
  with members:
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "pagerank beyond the web"
    - "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap"
    - "many sparse cuts via higher eigenvalues"
    - "social structure of facebook networks"
    - "approximating the exponential the lanczos method and an tilde o m time spectral algorithm for balanced separator"
    - "finding sparse cuts locally using evolving sets"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-8 (induced by source paper "parallel wavelet tree construction"):
  with members:
    - "parallelism in randomized incremental algorithms"
    - "sorting with asymmetric read and write costs"
    - "efficient algorithms with asymmetric read and write costs"
    - "wavelet trees meet suffix trees"
    - "counting triangles in massive graphs with mapreduce"
    - "parallel graph decompositions using random shifts"
    - "skew strikes back new developments in the theory of join algorithms"
    - "parallel triangle counting in massive streaming graphs"

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)