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: "efficient subgraph matching on billion node graphs"
  Abstract: The ability to handle large scale graph data is crucial to an increasing number of applications. Much work has been dedicated to supporting basic graph operations such as subgraph matching, reachability, regular expression matching, etc. In many cases, graph indices are employed to speed up query processing. Typically, most indices require either super-linear indexing time or super-linear indexing space. Unfortunately, for very large graphs, super-linear approaches are almost always infeasible. In this paper, we study the problem of subgraph matching on billion-node graphs. We present a novel algorithm that supports efficient subgraph matching for graphs deployed on a distributed memory store. Instead of relying on super-linear indices, we use efficient graph exploration and massive parallel computing for query processing. Our experimental results demonstrate the feasibility of performing subgraph matching on web-scale graph data.

Connected through hyperedge HE-1 (induced by source paper "fast detection of maximum common subgraph via deep q learning"):
  with members:
    - "simgnn a neural network approach to fast graph similarity computation"
    - "learning combinatorial embedding networks for deep graph matching"
    - "learning to route in similarity graphs"
    - "a unifying framework for spectrum preserving graph sparsification and coarsening"
    - "scalable gromov wasserstein learning for graph partitioning and matching"
    - "gromov wasserstein learning for graph matching and node embedding"
    - "a learning based branch and bound for maximum common subgraph problems"
    - "fast graph representation learning with pytorch geometric"

Connected through hyperedge HE-2 (induced by source paper "flow computation in temporal interaction networks"):
  with members:
    - "flow motifs in interaction networks"
    - "motifs in temporal networks"
    - "subgraph isomorphism in temporal networks"

Connected through hyperedge HE-3 (induced by source paper "modeling graphs with vertex replacement grammars"):
  with members:
    - "from louvain to leiden guaranteeing well connected communities"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "graph embedding techniques applications and performance a survey"
    - "learning hyperedge replacement grammars for graph generation"
    - "node2vec scalable feature learning for networks"
    - "growing graphs from hyperedge replacement graph grammars"
    - "an empirical comparison of the summarization power of graph clustering methods"

Connected through hyperedge HE-4 (induced by source paper "poirot aligning attack behavior with kernel audit records for cyber threat hunting"):
  with members:
    - "runtime analysis of whole system provenance"
    - "holmes real time apt detection through correlation of suspicious information flows"
    - "propatrol attack investigation via extracted high level tasks"
    - "sleuth real time attack scenario reconstruction from cots audit data"
    - "saql a stream based query system for real time abnormal system behavior detection"
    - "aiql enabling efficient attack investigation from system monitoring data"
    - "behavior query discovery in system generated temporal graphs"
    - "detection of early stage enterprise infection by mining large scale log data"

Connected through hyperedge HE-5 (induced by source paper "a survey and experimental analysis of distributed subgraph matching"):
  with members:
    - "foundations of modern query languages for graph databases"
    - "a selectivity based approach to continuous pattern detection in streaming graphs"
    - "communication steps for parallel query processing"
    - "enumerating subgraph instances using map reduce"
    - "worst case optimal join algorithms"

Connected through hyperedge HE-6 (induced by source paper "gsi gpu friendly subgraph isomorphism"):
  with members:
    - "optimizing subgraph queries by combining binary and worst case optimal joins"
    - "taming subgraph isomorphism for rdf query processing"
    - "gunrock a high performance graph processing library on the gpu"
    - "a new data layout for set intersection on gpus"
    - "semantics and complexity of sparql"

Connected through hyperedge HE-7 (induced by source paper "scalable pattern matching in metadata graphs via constraint checking"):
  with members:
    - "subgraph counting color coding beyond trees"
    - "node2vec scalable feature learning for networks"
    - "practical graph isomorphism ii"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"

Connected through hyperedge HE-8 (induced by source paper "efficient continuous multi query processing over graph streams"):
  with members:
    - "structural patterns of information cascades and their implications for dynamics and semantics"
    - "a selectivity based approach to continuous pattern detection in streaming graphs"
    - "continuous queries for multi relational graphs"

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)