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: "flow based local graph clustering with better seed set inclusion"
  Abstract: Flow-based methods for local graph clustering have received significant recent attention for their theoretical cut improvement and runtime guarantees. In this work we present two improvements for using flow-based methods in real-world semi-supervised clustering problems. Our first contribution is a generalized objective function that allows practitioners to place strict and soft penalties on excluding specific seed nodes from the output set. This feature allows us to avoid the tendency, often exhibited by previous flow-based methods, to contract a large seed set into a small set of nodes that does not contain all or even most of the seed nodes. Our second contribution is a fast algorithm for minimizing our generalized objective function, based on a variant of the push-relabel algorithm for computing preflows. We make our approach very fast in practice by implementing a global relabeling heuristic and employing a warm-start procedure to quickly solve related cut problems. In practice our algorithm is faster than previous related flow-based methods, and is also more robust in detecting ground truth target regions in a graph, thanks to its ability to better incorporate semi-supervised information about target clusters.

Connected through hyperedge HE-1 (induced by source paper "compressive sensing for cut improvement and local clustering"):
  with members:
    - "statistical guarantees for local graph clustering"
    - "power weighted shortest paths for clustering euclidean data"
    - "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"
    - "detecting overlapping communities from local spectral subspaces"
    - "computing heat kernel pagerank and a local clustering algorithm"
    - "uncovering the small community structure in large networks a local spectral approach"

Connected through hyperedge HE-2 (induced by source paper "localized flow based clustering in hypergraphs"):
  with members:
    - "hypergraph cuts with general splitting functions"
    - "landing probabilities of random walks for seed set expansion in hypergraphs"
    - "random walks on hypergraphs with edge dependent vertex weights"
    - "revisiting decomposable submodular function minimization with incidence relations"
    - "simplicial closure and higher order link prediction"
    - "submodular hypergraphs p laplacians cheeger inequalities and spectral clustering"
    - "decomposable submodular function minimization discrete and continuous"
    - "higher order organization of complex networks"

Connected through hyperedge HE-3 (induced by source paper "local hypergraph clustering using capacity releasing diffusion"):
  with members:
    - "capacity releasing diffusion for speed and locality"
    - "flow based algorithms for local graph clustering"
    - "a local spectral method for graphs with applications to improving graph partitions and exploring data graphs locally"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-4 (induced by source paper "learning resolution parameters for graph clustering"):
  with members:
    - "a parallel projection method for metric constrained optimization"
    - "correlation clustering generalized"
    - "capacity releasing diffusion for speed and locality"
    - "multiresolution consensus clustering in networks"
    - "the ground truth about metadata and community detection in networks"
    - "an optimization approach to locally biased graph algorithms"
    - "higher order organization of complex networks"
    - "a simple and strongly local flow based method for cut improvement"

Connected through hyperedge HE-5 (induced by source paper "statistical guarantees for local graph clustering"):
  with members:
    - "capacity releasing diffusion for speed and locality"
    - "an optimization approach to locally biased graph algorithms"
    - "parallel local graph clustering"
    - "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"

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.SI(Social and Information Networks)