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: "local graph clustering beyond cheeger s inequality"
  Abstract: Motivated by applications of large-scale graph clustering, we study random-walk-based LOCAL algorithms whose running times depend only on the size of the output cluster, rather than the entire graph. All previously known such algorithms guarantee an output conductance of $\tilde{O}(\sqrt{\phi(A)})$ when the target set $A$ has conductance $\phi(A)\in[0,1]$. In this paper, we improve it to $$\tilde{O}\bigg( \min\Big\{\sqrt{\phi(A)}, \frac{\phi(A)}{\sqrt{\mathsf{Conn}(A)}} \Big\} \bigg)\enspace, $$ where the internal connectivity parameter $\mathsf{Conn}(A) \in [0,1]$ is defined as the reciprocal of the mixing time of the random walk over the induced subgraph on $A$. #R##N#For instance, using $\mathsf{Conn}(A) = \Omega(\lambda(A) / \log n)$ where $\lambda$ is the second eigenvalue of the Laplacian of the induced subgraph on $A$, our conductance guarantee can be as good as $\tilde{O}(\phi(A)/\sqrt{\lambda(A)})$. This builds an interesting connection to the recent advance of the so-called improved Cheeger's Inequality [KKL+13], which says that global spectral algorithms can provide a conductance guarantee of $O(\phi_{\mathsf{opt}}/\sqrt{\lambda_3})$ instead of $O(\sqrt{\phi_{\mathsf{opt}}})$. #R##N#In addition, we provide theoretical guarantee on the clustering accuracy (in terms of precision and recall) of the output set. We also prove that our analysis is tight, and perform empirical evaluation to support our theory on both synthetic and real data. #R##N#It is worth noting that, our analysis outperforms prior work when the cluster is well-connected. In fact, the better it is well-connected inside, the more significant improvement (both in terms of conductance and accuracy) we can obtain. Our results shed light on why in practice some random-walk-based algorithms perform better than its previous theory, and help guide future research about local clustering.

Connected through hyperedge HE-1 (induced by source paper "local network community detection with continuous optimization of conductance and weighted kernel k means"):
  with members:
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "heat kernel based community detection"
    - "defining and evaluating network communities based on ground truth"
    - "empirical comparison of algorithms for network community detection"
    - "a local spectral method for graphs with applications to improving graph partitions and exploring data graphs locally"
    - "on the np completeness of some graph cluster measures"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

Connected through hyperedge HE-2 (induced by source paper "local community detection by seed expansion from conductance to weighted kernel 1 mean optimization"):
  with members:
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "heat kernel based community detection"
    - "defining and evaluating network communities based on ground truth"
    - "empirical comparison of algorithms for network community detection"
    - "a local spectral method for graphs with applications to improving graph partitions and exploring data graphs locally"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

Connected through hyperedge HE-3 (induced by source paper "random walks and evolving sets faster convergences and limitations"):
  with members:
    - "improved cheeger s inequality and analysis of local graph partitioning using vertex expansion and expansion profile"
    - "analysis of boolean functions"
    - "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap"
    - "on extracting common random bits from correlated sources on large alphabets"
    - "a local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning"
    - "finding sparse cuts locally using evolving sets"
    - "max cut and the smallest eigenvalue"

Connected through hyperedge HE-4 (induced by source paper "improved cheeger s inequality and analysis of local graph partitioning using vertex expansion and expansion profile"):
  with members:
    - "improved cheeger s inequality analysis of spectral partitioning algorithms through higher order spectral gap"
    - "many sparse cuts via higher eigenvalues"
    - "approximating the expansion profile and almost optimal local graph clustering"
    - "a local clustering algorithm for massive graphs and its application to nearly linear time graph partitioning"
    - "finding sparse cuts locally using evolving sets"

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)