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: "semi supervised eigenvectors for large scale locally biased learning"
  Abstract: In many applications, one has side information, e.g., labels that are provided in a semi-supervised manner, about a specific target region of a large data set, and one wants to perform machine learning and data analysis tasks "nearby" that prespecified target region. For example, one might be interested in the clustering structure of a data graph near a prespecified "seed set" of nodes, or one might be interested in finding partitions in an image that are near a prespecified "ground truth" set of pixels. Locally-biased problems of this sort are particularly challenging for popular eigenvector-based machine learning and data analysis tools. At root, the reason is that eigenvectors are inherently global quantities, thus limiting the applicability of eigenvector-based methods in situations where one is interested in very local properties of the data. #R##N#In this paper, we address this issue by providing a methodology to construct semi-supervised eigenvectors of a graph Laplacian, and we illustrate how these locally-biased eigenvectors can be used to perform locally-biased machine learning. These semi-supervised eigenvectors capture successively-orthogonalized directions of maximum variance, conditioned on being well-correlated with an input seed set of nodes that is assumed to be provided in a semi-supervised manner. We show that these semi-supervised eigenvectors can be computed quickly as the solution to a system of linear equations; and we also describe several variants of our basic method that have improved scaling properties. We provide several empirical examples demonstrating how these semi-supervised eigenvectors can be used to perform locally-biased learning; and we discuss the relationship between our results and recent machine learning algorithms that use global eigenvectors of the graph Laplacian.

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"
    - "network analysis of recurring youtube spam campaigns"
    - "finding endogenously formed communities"
    - "demon a local first discovery method for overlapping communities"

Connected through hyperedge HE-2 (induced by source paper "an optimization approach to locally biased graph algorithms"):
  with members:
    - "a simple and strongly local flow based method for cut improvement"
    - "think locally act locally detection of small medium sized and large communities in large networks"
    - "computing heat kernel pagerank and a local clustering algorithm"
    - "heat kernel based community detection"
    - "flow based algorithms for local graph clustering"
    - "social structure of facebook networks"
    - "the anatomy of the facebook social graph"
    - "empirical comparison of algorithms for network community detection"

Connected through hyperedge HE-3 (induced by source paper "structure and inference in annotated networks"):
  with members:
    - "clustering attributed graphs models measures and methods"
    - "phase transitions in semisupervised clustering of sparse networks"
    - "efficiently inferring community structure in bipartite networks"
    - "community structure detection in complex networks with partial background information"
    - "community detection in networks with node attributes"
    - "friendship networks and social status"
    - "social structure of facebook networks"
    - "active learning for node classification in assortative and disassortative networks"

Connected through hyperedge HE-4 (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"
    - "finding endogenously formed communities"
    - "demon a local first discovery method for overlapping communities"
    - "defining and evaluating network communities based on ground truth"
    - "a markov random walk under constraint for discovering overlapping communities in complex networks"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

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.LG(Machine Learning)