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: "a local spectral method for graphs with applications to improving graph partitions and exploring data graphs locally"
  Abstract: The second eigenvalue of the Laplacian matrix and its associated eigenvector are fundamental features of an undirected graph, and as such they have found widespread use in scientific computing, machine learning, and data analysis. In many applications, however, graphs that arise have several \emph{local} regions of interest, and the second eigenvector will typically fail to provide information fine-tuned to each local region. In this paper, we introduce a locally-biased analogue of the second eigenvector, and we demonstrate its usefulness at highlighting local properties of data graphs in a semi-supervised manner. To do so, we first view the second eigenvector as the solution to a constrained optimization problem, and we incorporate the local information as an additional constraint; we then characterize the optimal solution to this new problem and show that it can be interpreted as a generalization of a Personalized PageRank vector; and finally, as a consequence, we show that the solution can be computed in nearly-linear time. In addition, we show that this locally-biased vector can be used to compute an approximation to the best partition \emph{near} an input seed set in a manner analogous to the way in which the second eigenvector of the Laplacian can be used to obtain an approximation to the best partition in the entire input graph. Such a primitive is useful for identifying and refining clusters locally, as it allows us to focus on a local region of interest in a semi-supervised manner. Finally, we provide a detailed empirical evaluation of our method by showing how it can applied to finding locally-biased sparse cuts around an input vertex seed set in social and information networks.

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"
    - "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"
    - "detecting overlapping communities from local spectral subspaces"
    - "computing heat kernel pagerank and a local clustering algorithm"

Connected through hyperedge HE-2 (induced by source paper "local hypergraph clustering using capacity releasing diffusion"):
  with members:
    - "flow based local graph clustering with better seed set inclusion"
    - "capacity releasing diffusion for speed and locality"
    - "flow based algorithms for local graph clustering"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"

Connected through hyperedge HE-3 (induced by source paper "searching for polarization in signed graphs a local spectral approach"):
  with members:
    - "discovering polarized communities in signed networks"
    - "a survey of signed network mining in social media"
    - "heat kernel based community detection"

Connected through hyperedge HE-4 (induced by source paper "flow based local graph clustering with better seed set inclusion"):
  with members:
    - "capacity releasing diffusion for speed and locality"
    - "a simple and strongly local flow based method for cut improvement"
    - "heat kernel based community detection"
    - "flow based algorithms for local graph clustering"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-5 (induced by source paper "spectral sparsification of simplicial complexes for clustering and label propagation"):
  with members:
    - "near optimal sketching of low rank tensor regression"
    - "polynomial sized topological approximations using the permutahedron"
    - "tensor spectral clustering for partitioning higher order network structures"
    - "pagerank beyond the web"
    - "graph induced complex on point data"
    - "a simple combinatorial algorithm for solving sdd systems in nearly linear time"
    - "computing topological persistence for simplicial maps"
    - "applications of zigzag persistence to topological data analysis"

Connected through hyperedge HE-6 (induced by source paper "krylov subspace approximation for local community detection in large networks"):
  with members:
    - "overlapping community detection using superior seed set selection in social networks"
    - "hidden community detection in social networks"
    - "local network community detection with continuous optimization of conductance and weighted kernel k means"
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "detecting overlapping communities from local spectral subspaces"
    - "uncovering the small community structure in large networks a local spectral approach"
    - "overlapping community detection using neighborhood inflated seed expansion"
    - "heat kernel based community detection"

Connected through hyperedge HE-7 (induced by source paper "krylov subspace approximation for local community detection"):
  with members:
    - "overlapping community detection using superior seed set selection in social networks"
    - "hidden community detection in social networks"
    - "local network community detection with continuous optimization of conductance and weighted kernel k means"
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "detecting overlapping communities from local spectral subspaces"
    - "heat kernel based community detection"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "demon a local first discovery method for overlapping communities"

Connected through hyperedge HE-8 (induced by source paper "parallel local graph clustering"):
  with members:
    - "a simple and strongly local flow based method for cut improvement"
    - "distributed algorithms for finding local clusters using heat kernel pagerank"
    - "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"

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)