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 markov random walk under constraint for discovering overlapping communities in complex networks"
  Abstract: The detection of overlapping communities in complex networks has motivated recent research in relevant fields. Aiming to address this problem, we propose a Markov-dynamics-based algorithm, called UEOC, which means 'unfold and extract overlapping communities'. In UEOC, when identifying each natural community that overlaps, a Markov random walk method combined with a constraint strategy, which is based on the corresponding annealed network (degree conserving random network), is performed to unfold the community. Then, a cutoff criterion with the aid of a local community function, called conductance, which can be thought of as the ratio between the number of edges inside the community and those leaving it, is presented to extract this emerged community from the entire network. The UEOC algorithm depends on only one parameter whose value can be easily set, and it requires no prior knowledge of the hidden community structures. The proposed UEOC has been evaluated both on synthetic benchmarks and on some real-world networks, and has been compared with a set of competing algorithms. The experimental result has shown that UEOC is highly effective and efficient for discovering overlapping communities.

Connected through hyperedge HE-1 (induced by source paper "in a world that counts clustering and detecting fake social engagement at scale"):
  with members:
    - "overlapping community detection via local spectral clustering"
    - "uncovering the small community structure in large networks a local spectral approach"
    - "heat kernel based community detection"
    - "paying for likes understanding facebook like fraud using honeypots"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "semi supervised eigenvectors for large scale locally biased learning"
    - "network analysis of recurring youtube spam campaigns"
    - "finding endogenously formed communities"

Connected through hyperedge HE-2 (induced by source paper "uncovering the small community structure in large networks a local spectral approach"):
  with members:
    - "heat kernel based community detection"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "semi supervised eigenvectors for large scale locally biased learning"
    - "finding endogenously formed communities"
    - "demon a local first discovery method for overlapping communities"
    - "defining and evaluating network communities based on ground truth"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

Connected through hyperedge HE-3 (induced by source paper "detecting overlapping communities from local spectral subspaces"):
  with members:
    - "uncovering the small community structure in large networks a local spectral approach"
    - "heat kernel based community detection"
    - "demon a local first discovery method for overlapping communities"
    - "defining and evaluating network communities based on ground truth"
    - "a tutorial on spectral clustering"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

Connected through hyperedge HE-4 (induced by source paper "overlapping community detection via local spectral clustering"):
  with members:
    - "detecting overlapping communities from local spectral subspaces"
    - "uncovering the small community structure in large networks a local spectral approach"
    - "heat kernel based community detection"
    - "overlapping community detection in networks the state of the art and comparative study"
    - "a local spectral method for graphs with applications to improving graph partitions and exploring data graphs locally"

Connected through hyperedge HE-5 (induced by source paper "overlapping community detection in networks the state of the art and comparative study"):
  with members:
    - "towards linear time overlapping community detection in social networks"
    - "community detection using a neighborhood strength driven label propagation algorithm"
    - "slpa uncovering overlapping communities in social networks via a speaker listener interaction dynamic process"
    - "efficient and principled method for detecting communities in networks"
    - "empirical comparison of algorithms for network community detection"
    - "community structure in large networks natural cluster sizes and the absence of large well defined clusters"
    - "mapping the gnutella network properties of large scale peer to peer systems and implications for system design"

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)