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: "alid scalable dominant cluster detection"
  Abstract: Detecting dominant clusters is important in many analytic applications. The state-of-the-art methods find dense subgraphs on the affinity graph as the dominant clusters. However, the time and space complexity of those methods are dominated by the construction of the affinity graph, which is quadratic with respect to the number of data points, and thus impractical on large data sets. To tackle the challenge, in this paper, we apply Evolutionary Game Theory (EGT) and develop a scalable algorithm, Approximate Localized Infection Immunization Dynamics (ALID). The major idea is to perform Localized Infection Immunization Dynamics (LID) to find dense subgraph within local range of the affinity graph. LID is further scaled up with guaranteed high efficiency and detection quality by an estimated Region of Interest (ROI) and a carefully designed Candidate Infective Vertex Search method (CIVS). ALID only constructs small local affinity graphs and has a time complexity of O(C(a^*+ {\delta})n) and a space complexity of O(a^*(a^*+ {\delta})), where a^* is the size of the largest dominant cluster and C << n and {\delta} << n are small constants. We demonstrate by extensive experiments on both synthetic data and real world data that ALID achieves state-of-the-art detection quality with much lower time and space cost on single machine. We also demonstrate the encouraging parallelization performance of ALID by implementing the Parallel ALID (PALID) on Apache Spark. PALID processes 50 million SIFT data points in 2.29 hours, achieving a speedup ratio of 7.51 with 8 executors.

Connected through hyperedge HE-1 (induced by source paper "mining density contrast subgraphs"):
  with members:
    - "space and time efficient algorithm for maintaining dense subgraphs on one pass dynamic streams"
    - "deltacon a principled massive graph similarity function"
    - "dense subgraph maintenance under streaming edge weight updates for real time story identification"
    - "densest subgraph in streaming and mapreduce"

Connected through hyperedge HE-2 (induced by source paper "distributed bayesian piecewise sparse linear models"):
  with members:
    - "mobile big data analytics using deep learning and apache spark"
    - "rebuilding factorized information criterion asymptotically accurate marginal likelihood"
    - "mli an api for distributed machine learning"
    - "factorized asymptotic bayesian hidden markov models"
    - "a system for induction of oblique decision trees"

Connected through hyperedge HE-3 (induced by source paper "multi target tracking in multiple non overlapping cameras using constrained dominant sets"):
  with members:
    - "large scale image geo localization using dominant sets"
    - "performance measures and a data set for multi target multi camera tracking"
    - "interactive image segmentation using constrained dominant sets"
    - "deep residual learning for image recognition"
    - "person re identification by local maximal occurrence representation and metric learning"

Connected through hyperedge HE-4 (induced by source paper "finding theme communities from database networks"):
  with members:
    - "abacus frequent pattern mining based community discovery in multidimensional networks"
    - "community detection in networks with node attributes"
    - "truss decomposition in massive networks"
    - "defining and evaluating network communities based on ground truth"

Connected through hyperedge HE-5 (induced by source paper "finding theme communities from database networks from mining to indexing and query answering"):
  with members:
    - "abacus frequent pattern mining based community discovery in multidimensional networks"
    - "community detection in networks with node attributes"
    - "truss decomposition in massive networks"

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.DB(Databases)