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: "accurate location estimation of moving object with energy constraint a adaptive update algorithms to save data"
  Abstract: In research paper "Accurate estimation of the target location of object with energy constraint & Adaptive Update Algorithms to Save Data" one of the central issues in sensor networks is track the location, of moving object which have overhead of saving data, an accurate estimation of the target location of object with energy constraint .We do not have any mechanism which control and maintain data .The wireless communication bandwidth is also very limited. Some field which is using this technique are flood and typhoon detection, forest fire detection, temperature and humidity and ones we have these information use these information back to a central air conditioning and ventilation system. In this research paper, we propose protocol based on the prediction and adaptive based algorithm which is using less sensor node reduced by an accurate estimation of the target location. we are using minimum three sensor node to get the accurate position .We can extend it upto four or five to find more accurate location but we have energy constraint so we are using three with accurate estimation of location help us to reduce sensor node..We show that our tracking method performs well in terms of energy saving regardless of mobility pattern of the mobile target .We extends the life time of network with less sensor node. Once a new object is detected, a mobile agent will be initiated to track the roaming path of the object. The agent is mobile since it will choose the sensor closest to the object to stay. The agent may invite some nearby slave sensors to cooperatively position the object and inhibit other irrelevant (i.e., farther) sensors from tracking the object. As a result, the communication and sensing overheads are greatly reduced.

Connected through hyperedge HE-1 (induced by source paper "an effective approach for mobile ad hoc network via i watchdog protocol"):
  with members:
    - "study of humanoid push recovery based on experiments"
    - "machine learning approach for text and document mining"
    - "an effective approach for mobile ad hoc network via i watchdog protocol"
    - "real time face recognition using adaboost improved fast pca algorithm"

Connected through hyperedge HE-2 (induced by source paper "an effective approach for mobile ad hoc network via i watchdog protocol"):
  with members:
    - "study of humanoid push recovery based on experiments"
    - "an effective approach for mobile ad hoc network via i watchdog protocol"
    - "machine learning approach for text and document mining"
    - "real time face recognition using adaboost improved fast pca algorithm"

Connected through hyperedge HE-3 (induced by source paper "noisy distance measurements using 3 d localization with rb rf methods"):
  with members:
    - "machine learning approach for text and document mining"
    - "real time face recognition using adaboost improved fast pca algorithm"

Connected through hyperedge HE-4 (induced by source paper "machine learning approach for text and document mining"):
  with members:
    - "real time face recognition using adaboost improved fast pca algorithm"

Connected through hyperedge HE-5 (induced by source paper "enhanced ezw technique for compression of image by setting detail retaining pass number"):
  with members:
    - "real time face recognition using adaboost improved fast pca algorithm"

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)