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: "hitting and commute times in large graphs are often misleading"
  Abstract: Next to the shortest path distance, the second most popular distance function between vertices in a graph is the commute distance (resistance distance). For two vertices u and v, the hitting time H_{uv} is the expected time it takes a random walk to travel from u to v. The commute time is its symmetrized version C_{uv} = H_{uv} + H_{vu}. In our paper we study the behavior of hitting times and commute distances when the number n of vertices in the graph is very large. We prove that as n converges to infinty, hitting times and commute distances converge to expressions that do not take into account the global structure of the graph at all. Namely, the hitting time H_{uv} converges to 1/d_v and the commute time to 1/d_u + 1/d_v where d_u and d_v denote the degrees of vertices u and v. In these cases, the hitting and commute times are misleading in the sense that they do not provide information about the structure of the graph. We focus on two major classes of random graphs: random geometric graphs (k-nearest neighbor graphs, epsilon-graphs, Gaussian similarity graphs) and random graphs with given expected degrees (in particular, Erdos-Renyi graphs with and without planted partitions)

Connected through hyperedge HE-1 (induced by source paper "learning networks from random walk based node similarities"):
  with members:
    - "predicting positive and negative links with noisy queries theory practice"
    - "deepwalk online learning of social representations"
    - "near linear query complexity for graph inference"
    - "graph reconstruction via distance oracles"
    - "a tractable approach to finding closest truncated commute time neighbors in large graphs"
    - "fast incremental and personalized pagerank"

Connected through hyperedge HE-2 (induced by source paper "netlsd hearing the shape of a graph"):
  with members:
    - "verse versatile graph embeddings from similarity measures"
    - "struc2vec learning node representations from structural identity"
    - "heat kernel based community detection"
    - "deltacon a principled massive graph similarity function"

Connected through hyperedge HE-3 (induced by source paper "semantic measures for the comparison of units of language concepts or entities from text and knowledge base analysis"):
  with members:
    - "description and evaluation of semantic similarity measures approaches"
    - "distributional measures as proxies for semantic relatedness"
    - "roget s thesaurus and semantic similarity"
    - "distributional measures of semantic distance a survey"
    - "a new similarity measure for taxonomy based on edge counting"
    - "a semantic relatedness measure based on combined encyclopedic ontological and collocational knowledge"
    - "serimi resource description similarity rdf instance matching and interlinking"
    - "effectively integrating information content and structural relationship to improve the go based similarity measure between proteins"

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)