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: "eagr supporting continuous ego centric aggregate queries over large dynamic graphs"
  Abstract: In this work, we present EAGr, a system for supporting large numbers of continuous neighborhood-based ("ego-centric") aggregate queries over large, highly dynamic, and rapidly evolving graphs. Examples of such queries include computation of personalized, tailored trends in social networks, anomaly/event detection in financial transaction networks, local search and alerts in spatio-temporal networks, to name a few. Key challenges in supporting such continuous queries include high update rates typically seen in these situations, large numbers of queries that need to be executed simultaneously, and stringent low latency requirements. We propose a flexible, general, and extensible in-memory framework for executing different types of ego-centric aggregate queries over large dynamic graphs with low latencies. Our framework is built around the notion of an aggregation overlay graph, a pre-compiled data structure that encodes the computations to be performed when an update/query is received. The overlay graph enables sharing of partial aggregates across multiple ego-centric queries (corresponding to the nodes in the graph), and also allows partial pre-computation of the aggregates to minimize the query latencies. We present several highly scalable techniques for constructing an overlay graph given an aggregation function, and also design incremental algorithms for handling structural changes to the underlying graph. We also present an optimal, polynomial-time algorithm for making the pre-computation decisions given an overlay graph, and evaluate an approach to incrementally adapt those decisions as the workload changes. Although our approach is naturally parallelizable, we focus on a single-machine deployment and show that our techniques can easily handle graphs of size up to 320 million nodes and edges, and achieve update/query throughputs of over 500K/s using a single, powerful machine.

Connected through hyperedge HE-1 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism"):
  with members:
    - "storing and querying large scale spatio temporal graphs with high throughput edge insertions"
    - "parallel batch dynamic graph connectivity"
    - "demystifying graph databases analysis and taxonomy of data organization system designs and graph queries"
    - "graph processing on fpgas taxonomy survey challenges"
    - "slim fly a cost effective low diameter network topology"
    - "red blue pebbling revisited near optimal parallel matrix matrix multiplication"
    - "dynamic algorithms for the massively parallel computation model"
    - "fatpaths routing in supercomputers data centers and clouds with low diameter networks when shortest paths fall short"

Connected through hyperedge HE-2 (induced by source paper "g thinker big graph mining made easier and faster"):
  with members:
    - "effective techniques for message reduction and load balancing in distributed graph computation"
    - "nscale neighborhood centric large scale graph analytics in the cloud"
    - "distributed graphlab a framework for machine learning in the cloud"

Connected through hyperedge HE-3 (induced by source paper "catching worms trojan horses and pups unsupervised detection of silent delivery campaigns"):
  with members:
    - "detection of early stage enterprise infection by mining large scale log data"

Connected through hyperedge HE-4 (induced by source paper "on smart query routing for distributed graph querying with decoupled storage"):
  with members:
    - "the end of slow networks it s time for a redesign"
    - "spinner scalable graph partitioning in the cloud"
    - "fast exact shortest path distance queries on large networks by pruned landmark labeling"

Connected through hyperedge HE-5 (induced by source paper "emptyheaded a relational engine for graph processing"):
  with members:
    - "size bounds and query plans for relational joins"
    - "snap a general purpose network analysis and graph mining library"
    - "aggregations over generalized hypertree decompositions"
    - "join processing for graph patterns an old dog with new tricks"
    - "faq questions asked frequently"
    - "simd compression and the intersection of sorted integers"
    - "gym a multiround join algorithm in mapreduce"
    - "skew strikes back new developments in the theory of join algorithms"

Connected through hyperedge HE-6 (induced by source paper "emptyheaded boolean algebra based graph processing"):
  with members:
    - "size bounds and query plans for relational joins"
    - "snap a general purpose network analysis and graph mining library"
    - "simd compression and the intersection of sorted integers"
    - "skew strikes back new developments in the theory of join algorithms"
    - "communication steps for parallel query processing"
    - "leapfrog triejoin a worst case optimal join algorithm"
    - "decoding billions of integers per second through vectorization"
    - "worst case optimal join algorithms"

Connected through hyperedge HE-7 (induced by source paper "a selectivity based approach to continuous pattern detection in streaming graphs"):
  with members:
    - "a space efficient streaming algorithm for triangle counting using the birthday paradox"
    - "efficient subgraph matching on billion node graphs"
    - "gsketch on query estimation in graph streams"

Connected through hyperedge HE-8 (induced by source paper "supporting window analytics over large scale dynamic graphs"):
  with members:
    - "dagger a scalable index for reachability queries in large dynamic graphs"
    - "optimization of analytic window functions"
    - "k reach who is in your small world"

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)