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: "medians and beyond new aggregation techniques for sensor networks"
  Abstract: Wireless sensor networks offer the potential to span and monitor large geographical areas inexpensively. Sensors, however, have significant power constraint (battery life), making communication very expensive. Another important issue in the context of sensor-based information systems is that individual sensor readings are inherently unreliable. In order to address these two aspects, sensor database systems like TinyDB and Cougar enable in-network data aggregation to reduce the communication cost and improve reliability. The existing data aggregation techniques, however, are limited to relatively simple types of queries such as SUM, COUNT, AVG, and MIN/MAX. In this paper we propose a data aggregation scheme that significantly extends the class of queries that can be answered using sensor networks. These queries include (approximate) quantiles, such as the median, the most frequent data values, such as the consensus value, a histogram of the data distribution, as well as range queries. In our scheme, each sensor aggregates the data it has received from other sensors into a fixed (user specified) size message. We provide strict theoretical guarantees on the approximation quality of the queries in terms of the message size. We evaluate the performance of our aggregation scheme by simulation and demonstrate its accuracy, scalability and low resource utilization for highly variable input data sets.

Connected through hyperedge HE-1 (induced by source paper "streaming algorithms for bin packing and vector scheduling"):
  with members:
    - "tight lower bound for comparison based quantile summaries"
    - "a new and improved algorithm for online bin packing"
    - "fully dynamic bin packing with limited repacking"
    - "optimal quantile approximation in streams"
    - "closing the gap for makespan scheduling via sparsification techniques"
    - "a logarithmic additive integrality gap for bin packing"
    - "tight bounds for online vector scheduling"
    - "polynomiality for bin packing with a constant number of item types"

Connected through hyperedge HE-2 (induced by source paper "streaming quantiles algorithms with small space and update time"):
  with members:
    - "optimal quantile approximation in streams"
    - "xgboost a scalable tree boosting system"
    - "optimal tracking of distributed heavy hitters and quantiles"

Connected through hyperedge HE-3 (induced by source paper "sense scalable data acquisition from distributed sensors with guaranteed time coherence"):
  with members:
    - "benchmarking distributed stream processing engines"
    - "consistent streaming through time a vision for event stream processing"

Connected through hyperedge HE-4 (induced by source paper "tight lower bound for comparison based quantile summaries"):
  with members:
    - "optimal quantile approximation in streams"

Connected through hyperedge HE-5 (induced by source paper "moment based quantile sketches for efficient high cardinality aggregation queries"):
  with members:
    - "an efficient bandit algorithm for realtime multivariate optimization"
    - "macrobase prioritizing attention in fast data"
    - "spectrum estimation from samples"
    - "an experimental study of distributed quantile estimation"
    - "blinkdb queries with bounded errors and bounded response times on very large data"
    - "a method of moments for mixture models and hidden markov models"
    - "polynomial learning of distribution families"

Connected through hyperedge HE-6 (induced by source paper "sketching linear classifiers over data streams"):
  with members:
    - "introduction to online convex optimization"
    - "incremental skip gram model with negative sampling"
    - "algorithmic decision making and the cost of fairness"
    - "federated multi task learning"
    - "macrobase prioritizing attention in fast data"
    - "why should i trust you explaining the predictions of any classifier"
    - "heavy hitters via cluster preserving clustering"
    - "federated optimization distributed optimization beyond the datacenter"

Connected through hyperedge HE-7 (induced by source paper "optimal gossip algorithms for exact and approximate quantile computations"):
  with members:
    - "optimal quantile approximation in streams"
    - "breathe before speaking efficient information dissemination despite noisy limited and anonymous communication"
    - "analyzing network coding gossip made easy"

Connected through hyperedge HE-8 (induced by source paper "real time analytics algorithms and systems"):
  with members:
    - "the deletable bloom filter a new member of the bloom family"

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.DC(Distributed, Parallel, and Cluster Computing)